Loading [MathJax]/jax/output/SVG/jax.js
Overview

Finnish perspective on using synthetic health data to protect privacy: the PRIVASA project


  • The use of synthetic data could facilitate data-driven innovation across industries and applications. Synthetic data can be generated using a range of methods, from statistical modeling to machine learning and generative AI, resulting in datasets of different formats and utility. In the health sector, the use of synthetic data is often motivated by privacy concerns. As generative AI is becoming an everyday tool, there is a need for practice-oriented insights into the prospects and limitations of synthetic data, especially in the privacy sensitive domains. We present an interdisciplinary outlook on the topic, focusing on, but not limited to, the Finnish regulatory context. First, we emphasize the need for working definitions to avoid misplaced assumptions. Second, we consider use cases for synthetic data, viewing it as a helpful tool for experimentation, decision-making, and building data literacy. Yet the complementary uses of synthetic datasets should not diminish the continued efforts to collect and share high-quality real-world data. Third, we discuss how privacy-preserving synthetic datasets fall into the existing data protection frameworks. Neither the process of synthetic data generation nor synthetic datasets are automatically exempt from the regulatory obligations concerning personal data. Finally, we explore the future research directions for generating synthetic data and conclude by discussing potential future developments at the societal level.

    Citation: Tinja Pitkämäki, Tapio Pahikkala, Ileana Montoya Perez, Parisa Movahedi, Valtteri Nieminen, Tom Southerington, Juho Vaiste, Mojtaba Jafaritadi, Muhammad Irfan Khan, Elina Kontio, Pertti Ranttila, Juha Pajula, Harri Pölönen, Aysen Degerli, Johan Plomp, Antti Airola. Finnish perspective on using synthetic health data to protect privacy: the PRIVASA project[J]. Applied Computing and Intelligence, 2024, 4(2): 138-163. doi: 10.3934/aci.2024009

    Related Papers:

    [1] Omaima Alshanqiti, Ashutosh Pandey, Mani Shankar Pandey . A characterization of b-generalized skew derivations on a Lie ideal in a prime ring. AIMS Mathematics, 2024, 9(12): 34184-34204. doi: 10.3934/math.20241628
    [2] Abdul Nadim Khan, Shakir Ali . Involution on prime rings with endomorphisms. AIMS Mathematics, 2020, 5(4): 3274-3283. doi: 10.3934/math.2020210
    [3] Gurninder Singh Sandhu . On an identity involving generalized derivations and Lie ideals of prime rings. AIMS Mathematics, 2020, 5(4): 3472-3479. doi: 10.3934/math.2020225
    [4] Abu Zaid Ansari, Faiza Shujat, Ahlam Fallatah . Generalized differential identities on prime rings and algebras. AIMS Mathematics, 2023, 8(10): 22758-22765. doi: 10.3934/math.20231159
    [5] Shakir Ali, Amal S. Alali, Sharifah K. Said Husain, Vaishali Varshney . Symmetric n-derivations on prime ideals with applications. AIMS Mathematics, 2023, 8(11): 27573-27588. doi: 10.3934/math.20231410
    [6] Hengbin Zhang . Automorphism group of the commuting graph of 2×2 matrix ring over Zps. AIMS Mathematics, 2021, 6(11): 12650-12659. doi: 10.3934/math.2021729
    [7] Muhammad Umar Mirza, Rukhshanda Anjum, Maged Z. Youssef, Turki Alsuraiheed . A comprehensive study on fuzzy and crisp graph indices: generalized formulae, proximity and accuracy analysis. AIMS Mathematics, 2023, 8(12): 30922-30939. doi: 10.3934/math.20231582
    [8] Fenhong Li, Liang Kong, Chao Li . Non-global nonlinear mixed skew Jordan Lie triple derivations on prime -rings. AIMS Mathematics, 2025, 10(4): 7795-7812. doi: 10.3934/math.2025357
    [9] Gurninder S. Sandhu, Deepak Kumar . A note on derivations and Jordan ideals of prime rings. AIMS Mathematics, 2017, 2(4): 580-585. doi: 10.3934/Math.2017.4.580
    [10] Gurninder S. Sandhu, Deepak Kumar . Correction: A note on derivations and Jordan ideals in prime rings. AIMS Mathematics, 2019, 4(3): 684-685. doi: 10.3934/math.2019.3.684
  • The use of synthetic data could facilitate data-driven innovation across industries and applications. Synthetic data can be generated using a range of methods, from statistical modeling to machine learning and generative AI, resulting in datasets of different formats and utility. In the health sector, the use of synthetic data is often motivated by privacy concerns. As generative AI is becoming an everyday tool, there is a need for practice-oriented insights into the prospects and limitations of synthetic data, especially in the privacy sensitive domains. We present an interdisciplinary outlook on the topic, focusing on, but not limited to, the Finnish regulatory context. First, we emphasize the need for working definitions to avoid misplaced assumptions. Second, we consider use cases for synthetic data, viewing it as a helpful tool for experimentation, decision-making, and building data literacy. Yet the complementary uses of synthetic datasets should not diminish the continued efforts to collect and share high-quality real-world data. Third, we discuss how privacy-preserving synthetic datasets fall into the existing data protection frameworks. Neither the process of synthetic data generation nor synthetic datasets are automatically exempt from the regulatory obligations concerning personal data. Finally, we explore the future research directions for generating synthetic data and conclude by discussing potential future developments at the societal level.



    The algebra of derivations and generalized derivations play a crucial role in the study of functional identities and their applications. There are many generalizations of derivations viz., generalized derivations, multiplicative generalized derivations, skew generalized derivations, bgeneralized derivations, etc. The notion of bgeneralized derivation was first introduced by Koșan and Lee [17]. The most important and systematic research on the bgeneralized derivations have been accomplished in [11,17,22,26] and references therein. In this manuscript, we characterize bgeneralized derivations which are strong commutative preserving (SCP) on R. Moreover, we also discuss and characterize bgeneralized derivations involving certain differential/functional identities on rings possessing involution.

    In the early nineties, after a memorable work on the structure theory of rings, a tremendous work has been established by Brešar considering centralizing mappings, commuting mappings, commutativity preserving (CP) mappings and strong commutativity preserving (SCP) mappings on some appropriate subset of rings. Since then it became a tempting research idea in the matrix theory/operator theory/ring theory for algebraists. Commutativity preserving (CP) maps were introduced and studied by Watkins [32] and further extended to SCP by Bell and Mason [6]. Inspired by the concept of SCP maps, Bell and Daif [5] demonstrated the commutativity of (semi-)prime rings possessing derivations and endomorphisms on right ideals (see also [27] and references therein). In [12], Deng and Ashraf studied strong commutativity preserving maps in more general context as follows: Let R be a semiprime ring. If R admits a mapping ψ and a derivation δ on R such that [ψ(r),δ(v)]=[r,v] for every r,vR, then R is commutative. In 1994, Brešar and Miers [7] characterized an additive map f:RR satisfying SCP on a semiprime ring R and showed that f is of the form f(r)=λr+μ(r) for every rR, where λC, λ2=1 and μ:RR is an additive map. Later, Lin and Liu [23] extended this result to Lie ideals of prime rings. Chasing to this, several techniques have been developed to investigate the behavior of strong commutativity preserving maps (SCP) using restrictions on polynomials, invoking derivations, generalized derivations, etcetera. An account of work has been done in the literature [3,10,12,13,15,20,21,23,24,25,27,30,31].

    On the other hand, the study of additive maps on rings possessing involution was initiated by Brešar et al. [7] and they characterized the additive centralizing mappings on the skew-symmetric elements of prime rings possessing involution. In the same vein, Lin and Liu [24] describe SCP additive maps on skew-symmetric elements of prime rings possessing involution. Later, Liau et al. [21] improved the above mentioned result for non-additive SCP mappings. Interestingly, in 2015 Ali et al. [1] studied the SCP maps in different way on rings possessing involution. They established the commutativity of prime ring of charateristic not two possessing second kind of involution satisfying [δ(r),δ(r)][r,r]=0 for every rR, where δ is a nonzero derivation of R. Recently, Khan and Dar [8] improved this result by studying the case of generalized derivations.

    Motivated by the above presented work, in this manuscript we have characterized bgeneralized derivations on prime rings possessing involution. Moreover, we also present some examples in support of our main theorems.

    Throughout the manuscript unless otherwise stated, R is a prime ring with center Z(R), Q is the maximal right ring of quotients, C=Z(Q) is the center of Q usually known as the extended centroid of R and is a field. "A ring R is said to be 2 torsion free if 2r=0 (where rR) implies r=0". The characteristic of R is represented by char(R). "A ring R is called a prime ring if rRv=(0) (where r,vR) implies r=0 or v=0 and is called a semiprime ring in case rRr=(0) implies r=0". "An additive map rr of R into itself is called an involution if (i) (rv)=vr and (ii) (r)=r hold for all r,vR. A ring equipped with an involution is known as a ring with involution or a ring. An element r in a ring with involution is said to be hermitian/symmetric if r=r and skew-hermitian/skew-symmetric if r=r". The sets of all hermitian and skew-hermitian elements of R will be denoted by W(R) and K(R), respectively. "If R is 2torsion free, then every rR can be uniquely represented in the form 2r=h+k where hW(R) and kK(R). Note that in this case r is normal, i.e., rr=rr, if and only if h and k commute. If all elements in R are normal, then R is called a normal ring". An example is the ring of quaternions. "The involution is said to be of the first kind if Z(R)W(R), otherwise it is said to be of the second kind. In the later case it is worthwhile to see that K(R)Z(R)(0)". We refer the reader to [4,14] for justification and amplification for the above mentioned notations and key definitions.

    For r,vR, the commutator of r and v is defined as [r,v]=rvvr. We say that a map f:RR preserves commutativity if [f(r),f(v)]=0 whenever [r,v]=0 for all r,vR. Following [7], "let S be a subset of R, a map f:RS is said to be strong commutativity preserving (SCP) on S if [f(r),f(v)]=[r,v] for all r,vS". Following [33], "an additive mapping T:RR is said to be a left (respectively right) centralizer (multiplier) of R if T(rv)=T(r)v (respectively T(rv)=rT(v)) for all r,vR. An additive mapping T is called a centralizer in case T is a left and a right centralizer of R". In ring theory it is more common to work with module homomorphisms. Ring theorists would write that T:RR is a homomorphism of a ring module R into itself. For a prime ring R all such homomorphisms are of the form T(r)=qr for all rR, where qQ (see Chapter 2 in [4]). "An additive mapping δ:RR is said to be a derivation on R if δ(rv)=δ(r)v+rδ(v) for all r,vR". It is well-known that every derivation of R can be uniquely extended to a derivation of Q. "A derivation δ is said to be Qinner if there exists αQ such that δ(r)=αrrα for all rR. Otherwise, it is called Qouter (δ is not inner)". "An additive map H:RR is called a generalized derivation of R if there exists a derivation δ of R such that H(rv)=H(r)v+rδ(v) for all r,vR". The derivation δ is uniquely determined by H and is called the associated derivation of H. The concept of generalized derivations covers the both the concepts of a derivation and a left centralizer. We would like to point out that in [19] Lee proved that "every generalized derivation can be uniquely extended to a generalized derivation of Q and thus all generalized derivations of R will be implicitly assumed to be defined on the whole Q".

    The recent concept of generalized derivations were introduced by Koșan and Lee [17], namely, bgeneralized derivations which was defined as follows: "An additive mapping H:RQ is called a (left) bgeneralized derivation of R associated with δ, an additive map from R to Q, if H(rv)=H(r)v+brδ(v) for all r,vR, where bQ". Also they proved that if "R is a prime ring and 0bQ, then the associated map δ is a derivation i.e., δ(rv)=δ(r)v+rδ(v) for all r,vR". It is easy to see that every generalized derivation is a 1generalized derivation. Also, the mapping rRαr+brcQ for a,b,cQ is a bgeneralized derivation of R, which is known as inner bgeneralized derivation of R. In spite of this, they also characterized bgeneralized derivation. That is, every bgeneralized derivation H on a semiprime ring R is of the form H(r)=αr+bδ(r) for all rR, where a,bQ. Following important facts are frequently used in the proof of our results:

    Fact 2.1 ([1,Lemma 2.1]). "Let R be a prime ring with involution such that char(R)2. If K(R)Z(R)(0) and R is normal, then R is commutative."

    Fact 2.2. "Let R be a ring with involution such that char(R)2. Then every rR can uniquely represented as 2r=w+s, where wW(R) and sK(R)."

    Fact 2.3 ([17,Theorem 2.3]). "Let R be a semiprime ring, bQ, and let H:RQ be a bgeneralized derivation associated with δ. Then δ is a derivation and there exists αQ such that H(r)=αr+bδ(r) for every rR."

    Fact 2.4 ([8,Lemma 2.2]). "Let R be a non-commutative prime ring with involution of the second kind such that char(R)2. If R admits a derivation δ:RR such that [δ(w),w]=0 for every wW(R), then δ(Z(R))=(0)."

    We need a well-known lemma due to Martindale [28], stated below in a form, convenient for our purpose.

    Lemma 2.1 ([28,Theorem 2(a)]). "Let R be a prime ring and ai,bi,cj,djQ. Suppose that mi=1airbi+nj=1cjrdj=0 for all rR. If a1,,am are Cindependent, then each bi is a Clinear combination of d1,,dn. If b1,,bm are Cindependent, then each ai is a Clinear combination of c1,,cn."

    We need Kharchenko's theorem for differential identities to prime rings [16]. The lemma below is a special case of [16,Lemma 2].

    Lemma 2.2 ([16,Lemma 2]). "Let R be a prime ring and ai,bi,cj,djQ and δ a Qouter derivation of R. Suppose that mi=1airbi+nj=1cjδ(r)dj=0 for all rR. Then mi=1airbi=0=nj=1cjrdj for all rR."

    We begin with the following Lemma which is needed for developing the proof of our theorems:

    Lemma 3.1. Let R be a non-commutative prime ring of characteristic different from two with involuation of the second kind. If for any αR, [αr,αr][r,r]=0 for every rR, then αC and α2=1.

    Proof. For any rR, we have

    [αr,αr][r,r]=0. (3.1)

    This can also be written as

    α2[r,r]+α[r,α]r+α[α,r]r[r,r]=0 (3.2)

    for every rR. Replace r by r+s in above equation, where sK(R)Z(R), we get

    α2[r,r]+α[r,α]rα[r,α]s+α[α,r]r+α[α,r]s[r,r]=0 (3.3)

    for every rR and sK(R)Z(R). In view of (3.2), we have

    α[α,r]s+α[α,r]s=α[α,r+r]s=0 (3.4)

    for every rR and sK(R)Z(R). Since the involution is of the second kind, so we have

    α[α,r+r]=0 (3.5)

    for every rR. For r=w+s, where wW(R) and sK(R), observe that

    α[α,w]=0 (3.6)

    for every wW(R). Substitute ss for w in above expression, we get

    α[α,s]=0 (3.7)

    for every sK(R), since the involution is of the second kind. Observe from Fact 2.2 that 2α[α,r]=α[α,2r]=α[α,w+s]=α[α,w]+α[α,s]=0. Thus we have αC and, by our hypothesis, (α21)[r,r]=0, for every rR. By the primeness of R, it follows that either α2=1 or [r,r]=0, for any rR. Thus we are led to the required conclusion by Fact 2.1.

    Theorem 3.1. Let R be a non-commutative prime ring of characteristic different from two. If H is a non-zero bgeneralized derivation on R associated with a derivation δ on R and ψ is a non-zero map on R satisfying [ψ(r),H(v)]=[r,v] for every r,vR. Then there exists 0λC and an additive map μ:RC such that H(r)=λr, ψ(r)=λ1r+μ(r), for any rR.

    Proof. Notice that if either δ=0 or b=0, the map H reduces to a centralizer, that is H(v)=αv, for any vR. Then the conclusion follows as a reduced case of [25,Theorem 1.1]. Hence, in the rest of the proof we assume both b0 and δ0. By Fact 2.3, there exists αH such that H(r)=αr+bδ(r) for every rR. By the hypothesis

    [ψ(r),αv+bδ(v)]=[r,v] (3.8)

    for every r,vR. Taking of vz for v in above expression gives

    (αv+bδ(v))[ψ(r),z]+[ψ(r),bvδ(z)]=v[r,z] (3.9)

    for every r,v,zR.

    Suppose firstly that δ is not an inner derivation of R. In view of (3.9) and Lemma 2.2, we observe that

    (αv+bv)[ψ(r),z]+[ψ(r),bvz]=v[r,z] (3.10)

    for every r,v,z,v,zR. In particular, for v=0 we have

    bv[ψ(r),z]=0 (3.11)

    for every r,z,vR. By the primeness of R and since b0, it follows that ψ(r)Z(R), for any rR. On the other hand, by ψ(r)Z(R) and (3.8) one has that [r,v]=0 for any r,vR, which is a contradiction since R is not commutative.

    Therefore, we have to consider the only case when there is qH such that δ(r)=[r,q], for every rR. Thus we rewrite (3.10) as follows

    ((αbq)v+bvq)[ψ(r),z]+[ψ(r),bvzqbvqz]=v[r,z] (3.12)

    that is

    {(αbq)vψ(r)+bvqψ(r)ψ(r)bvqvr}z+{bqvαv}zψ(r)+ψ(r)bvzqbvzqψ(r)+vzr=0 (3.13)

    for every r,v,zR.

    Suppose there exists vR such that {bv,v} are linearly C-independent. From relation (3.13) and Lemma 2.1, it follows that, for any rR, both r and qψ(r) are C-linear combinations of {1,q,ψ(r)}. In other words, there exist α1,α2,α3,β1,β2,β3C, depending by the choice of rR, such that

    r=α1+α2ψ(r)+α3q (3.14)

    and

    qψ(r)=β1+β2ψ(r)+β3q. (3.15)

    Notice that, for α2=0, relation (3.14) implies that q commutes with element rR. On the other hand, in case α20, by (3.14) we have that

    ψ(r)=α12(rα1α3q). (3.16)

    Then, by using (3.16) in (3.15), it follows that

    β1+β3q=α12(qβ2)(rα1α3q). (3.17)

    So, by commuting (3.17) with q, we get α12(qβ2)[r,q]=0, implying that [r,q]=0 in any case.

    Therefore q commutes with any element of R and this contradicts the fact that δ0. Therefore, for any vR, {v,bv} must be linearly C-dependent. In this case a standard argument shows that bC, which implies that H(r)=(αbq)r+r(bq), for any rR. Hence H is a generalized derivation of R and once again the result follows from [25,Theorem 1.1].

    The following theorem is a generalization of [8,Theorem 2.3].

    Theorem 3.2. Let R be a non-commutative prime ring with involution of the second kind of characteristic different from two. If H is a bgeneralized derivation on R associated with a derivation δ on R such that [H(r),H(r)]=[r,r] for every rR, then there exists λC such that λ2=1 and H(r)=λr for every rR.

    Proof. By the given hypothesis, we have

    [H(r),H(r)][r,r]=0 for all rR. (3.18)

    Taking r as r+v in (3.18) to get

    [H(r),H(v)]+[H(v),H(r)][r,v][v,r]=0 (3.19)

    for every r,vR. Substitute vs for v, where sK(R)Z(R), in above relation, we obtain

    0=[H(r),H(v)]s[H(r),bv]δ(s)+[H(v),H(r)]s+[bv,H(r)]δ(s)+[r,v]s[v,r]s (3.20)

    for every r,vR and sK(R)Z(R). Multiply (3.19) with s and combine with (3.20) to get

    2[H(v),H(r)]s2[v,r]s[H(r),bv]δ(s)+[bv,H(r)]δ(s)=0 (3.21)

    for every r,vR and sK(R)Z(R). Again substitute v as vs in (3.21), we get

    0=2[H(v),H(r)]s2+2[bv,H(r)]δ(s)s2[v,r]s2+[H(r),bv]δ(s)s+[bv,H(r)]δ(s)s (3.22)

    for every r,vR and sK(R)Z(R). In view of (3.21), we have

    2[bv,H(r)]δ(s)s+2[H(r),bv]δ(s)s=0 (3.23)

    for every r,vR and sK(R)Z(R). Since char(R)2 and the involution is of the second kind, so [bv,H(r)]+[H(r),bv]=[H(r),bv][H(r),bv]=0 for every r,vR or δ(s)s=0 for every sK(R)Z(R). Observe that s=0 is also implies δ(s)=0 for every sK(R)Z(R). Assume that δ(s)0, therefore we have

    [H(r),bv][H(r),bv]=0 (3.24)

    for every r,vR. Taking r=v=w+s in above expression, we obtain

    [H(s),bw][H(w),bs]=0 (3.25)

    for every wW(R) and sK(R). Replace s by s in (3.25), we get

    [H(s),bw][H(w),b]s=0 (3.26)

    for every wW(R) and sK(R)Z(R). Substitute ss for w in last expression, we get

    [H(s),bs]s[H(s),b]s2+b[b,s]δ(s)s=0 (3.27)

    for every sK(R) and sK(R)Z(R). One can see from (3.24) that [H(s),bs]=0 and [H(s),b]s=0 for every sK(R) and sK(R)Z(R). This reduces (3.27) into

    b[b,s]δ(s)s=0 (3.28)

    for every sK(R) and sK(R)Z(R). This implies either b[b,s]=0 for every sK(R) or δ(s)=0 for every sK(R)Z(R). Suppose b[b,s]=0 for every sK(R). Take s=w0s and use the fact that the involution is of the second kind, we get b[b,w0]=0 for every w0W(R). An application of Fact 2.2 yields bC. One can see from (3.27) that b[H(s),s]=0 for every sK(R) and sK(R)Z(R). If b=0 and in light of Fact 2.3, H has the following form: H(r)=αr, for some fixed element αH. Thus, by Lemma 3.1 and since R is not commutative, we get the required conclusion αC and α2=1.

    So we assume b0 and [H(s),s]=0 for every sK(R) and sK(R)Z(R). Again taking s as w0s and making use of Fact 2.2 in last relation gives H(s)Z(R) for every sK(R)Z(R). Next, take r=w and v=s in (3.19), we get

    [H(s),H(w)]+[w,s]=0 (3.29)

    for every sK(R) and wW(R). Substitute w0s for s in above relation, we get

    [H(w0s),H(w)]+[w,w0]s=0 (3.30)

    for every sK(R)Z(R) and w,w0W(R). It follows from the hypothesis that

    [H(w0),H(w)]s+[bw0,H(w)]δ(s)+[w,w0]s=0 (3.31)

    for every sK(R)Z(R) and w,w0W(R). For w0=w, we have

    b[H(w),w]δ(s)=0 (3.32)

    for every sK(R)Z(R) and wW(R). Since δ(s)0 and b0, so [H(w),w]=0 for every wW(R). Since bC, so we observe from (3.25) that

    [H(s),w][H(w),s]=0 (3.33)

    for every wW(R) and sK(R). Repalcement of s by sh in above expression and making use of H(s)Z(R) for every sK(R)Z(R) and b0 yields [δ(w),w]=0 for every wW(R). In light of Fact 2.4, we have δ(s)=0 for every sK(R)Z(R). Finally, we suppose δ(s)=0 and substitute v by vs in (3.19), we obtain

    [H(r),H(v)]s+[H(v),H(r)]s+[r,v]s[v,r]s=0 (3.34)

    for every r,vR and sK(R)Z(R). Combination of (3.19) and (3.34) gives

    ([H(v),H(r)][v,r])s=0 (3.35)

    for every r,vR and sK(R)Z(R). This implies that

    [H(v),H(r)][v,r]=0 (3.36)

    for every r,vR. In particular

    [H(r),H(v)][r,v]=0 (3.37)

    for every r,vR. As a special case of Theorem 3.1, H is of the form H(r)=λr, where λC and λ2=1.

    The following theorem is a generalization of [8,Theorem 2.4].

    Theorem 3.3. Let R be a non-commutative prime ring with involution of the second kind of characteristic different from two. If H is a bgeneralized derivation on R associated with a derivation δ on R such that [H(r),δ(r)]=[r,r] for every rR, then there exists λC such that H(r)=λr for every rR.

    Proof. By the hypothesis, we have

    [H(r),δ(r)][r,r]=0 (3.38)

    for every rR. The derivation δ satisfies δ(R)Z(R), otherwise the hypothesis [H(r),δ(r)]=[r,r] for every rR, would reduce to [r,r]=0 for all rR, and, therefore R would be commutative, by Fact 2.1. A linearization of (3.38) yields

    [H(r),δ(v)]+[H(v),δ(r)][r,v][v,r]=0 (3.39)

    for every r,vR. Replace r by zZ(R) in (3.39), we obtain [H(z),δ(v)]=0 for every vR and zZ(R). Observe from [18,Theorem 2] that H(z)Z(R) for every zZ(R). Now take r as rs in (3.38) and suppose δ(s)0, we get

    0=[H(r),δ(r)]s2[H(r),r]δ(s)sb[r,δ(r)]δ(s)s[b,δ(r)]rδ(s)sb[r,r]δ(s)2[b,r]rδ(s)2+[r,r]s2 (3.40)

    for every rR and sK(R)Z(R). In view of (3.38), we have

    0=[H(r),r]δ(s)sb[r,δ(r)]δ(s)s[b,δ(r)]rδ(s)sb[r,r]δ(s)2[b,r]rδ(s)2 (3.41)

    for every rR and sK(R)Z(R). Replace r by r+s in last expression and use the fact that H(z)Z(R) for every zZ(R), we get

    0=[H(r),r]δ(s)sb[r,δ(r)]δ(s)s[b,δ(r)]rδ(s)sb[r,r]δ(s)2[b,r]rδ(s)2[b,δ(r)]δ(s)s2[b,r]δ(s)2s (3.42)

    for every rR and sK(R)Z(R). Observe from (3.41)

    [b,δ(r)]δ(s)s2+[b,r]δ(s)2s=0 (3.43)

    for every rR and sK(R)Z(R). Replace r by rs and use (3.43), we get [b,r]δ(s)2s=0 This forces that [b,r]=0 for every rR since δ(s)0. One can easily obtain from last relation that bC. On the other hand

    0=H(s)[r,δ(r)]sb[δ(r),δ(r)]s2[b,δ(r)]δ(r)s2H(s)[r,r]δ(s)b[δ(r),r]δ(s)s[b,r]δ(r)δ(s)s+[r,r]s2 (3.44)

    for every rR and sK(R)Z(R). Since bC, so we have

    0=H(s)[r,δ(r)]sb[δ(r),δ(r)]s2H(s)[r,r]δ(s)b[δ(r),r]δ(s)s+[r,r]s2 (3.45)

    for every rR and sK(R)Z(R). Now substitute w and s for r and v in (3.39), respectively. This yields

    [H(s),δ(w)][H(w),δ(s)]+2[s,w]=0 (3.46)

    for every wW(R) and sK(R). Take s as sw in last relation and use bC, we see that

    H(s)[w,δ(w)][H(w),w]δ(s)=0 (3.47)

    for every wW(R) and sK(R)Z(R). On the other hand

    b[h,δ(w)]δ(s)[H(w),w]δ(s)=0 (3.48)

    for every wW(R) and sK(R)Z(R). In view of (3.47) and (3.48), we get

    (H(s)+bδ(s))[w,δ(w)]=0 (3.49)

    for every wW(R) and sK(R)Z(R). Since H(s)Z(R), δ(s)Z(R) and bZ(R), so either H(s)+bδ(s)=0 for every sK(R)Z(R) or [w,δ(w)]=0 for every wW(R). If [w,δ(w)]=0 for every wW(R), then δ(s)=0 for every sK(R)Z(R) from Fact 2.4. Therefore consider H(s)=bδ(s) for every sK(R)Z(R) and use it in (3.45), we obtain

    0=b[r,δ(r)]δ(s)sb[δ(r),δ(r)]s2+b[r,r]δ(s)2b[δ(r),r]δ(s)s+[r,r]s2 (3.50)

    for every rR and sK(R)Z(R). For r=w, we have

    2b[h,δ(h)]δ(s)s=0 (3.51)

    for every wW(R) and sK(R)Z(R). Thus b=0 or [w,δ(w)]=0 for every wW(R) or δ(s)=0 for every sK(R)Z(R). If b=0 and since s is not a zero-divisor, the relation (3.50) reduces to [r,r]=0, for every rR. Thus the commutativity of R follows from Fact 2.1, a contradiction. The rest of two relations yields δ(s)=0 for every sK(R)Z(R). Finally consider δ(s)=0 and replace r by rs in (3.38) and use the facts that the involution is of the second kind, we see that

    [H(r),δ(v)][H(v),δ(r)][r,v]+[v,r]=0 (3.52)

    for every r,vR. Combination of (3.39) and (3.52) yields

    [H(r),δ(v)][r,v]=0 (3.53)

    for every r,vR. In particular,

    [H(r),δ(v)][r,v]=0 (3.54)

    for every r,vR. In view of Theorem 3.1, there exist 0λC and an additive map μ:RC such that H(r)=λr and δ(r)=λ1r+μ(r) for every rR, where δ=δ. Commute the latter case with r, we get [δ(r),r]=0 for every rR. Since δ=δ0, so R is commutative from [29,Lemma 3], this leads to again a contradiction. This completes the proof of the theorem.

    The following example shows that the condition of the second kind involution is essential in Theorems 3.2 and 3.3. This example collected from [2,Example 1].

    Example 4.1. Let

    R={(β1β2β3β4)|β1,β2,β3,β4Z},

    which is of course a prime ring with ususal addition and multiplication of matrices, where Z is the set of integers. Define mappings H,δ,:RR such that

    H(β1β2β3β4)=(0β2β30),
    δ(β1β2β3β4)=(0β2β30),

    and a fixed element

    b=(1001),
    (β1β2β3β4)=(β4β2β3β1).

    Obviously,

    Z(R)={(β100β1)|β1Z}.

    Then r=r for every rZ(R), and hence Z(R)W(R), which shows that the involution is of the first kind. Moreover, H, δ are nonzero bgeneralized derivation and associated derivation with fixed element b defined as above, such that the hypotheses in Theorems 3.2 and 3.3 are satisfied but H is not in the form H(r)=λr for every rR. Thus, the hypothesis of the second kind involution is crucial in our results.

    We conclude the manuscript with the following example which reveals that Theorems 3.2 and 3.3 cannot be extended to semiprime rings.

    Example 4.2. Let (R,) be a ring with involution as defined above, which admits a bgeneralized derivation H, where δ is an associated nonzero derivation same as above and R1=C with the usual conjugation involution . Next, let S=R×R1 and define a bgeneralized derivation G on S by G(r,v)=(H(r),0) associated with a derivation D defined by D(r,v)=(δ(r),0). Obviously, (S,τ) is a semiprime ring with involution of the second kind such that τ(r,v)=(r,v). Then the bgeneralized derivation G satisfies the requirements of Theorems 3.2 and 3.3, but G is not in the form G(r)=λr for every rR, and R is not commutative. Hence, the primeness hypotheses in our results is not superfluous.

    We recall that "a generalized skew derivation is an additive mapping G:RR satisfying the rule G(rv)=G(r)v+ζ(r)(v) for every r,vR, where is an associated skew derivation of R and ζ is an automorphism of R". Following [9], De Filippis proposed the new concept for further research and he defined the following: "Let R be an associative algebra, bQ, be a linear mapping from R to itself, and ζ be an automorphism of R. A linear mapping G:RR is called an Xgeneralized skew derivation of R, with associated term (b,ζ,) if G(rv)=G(r)v+bζ(r)(v) for every r,vR". It is clear from both definitions, the notions of Xgeneralized skew derivation, generalize both generalized skew derivations and skew derivations. Hence, every Xgeneralized skew derivation is a generalized skew derivation as well as a skew derivation, but the converse statement is not true in general.

    Actuated by the concept specified by De Filippis [9] and having regard to our main theorems, the following are natural problems.

    Question 5.1. Let R be a (semi)-prime ring and L be a Lie ideal of R. Next, let F and G be two Xgeneralized skew derivation with an associated skew derivation of R such that

    [F(r),G(v)]=[r,v],for everyr,vL.

    Then what we can say about the behaviour of F and G or about the structure of R?

    Question 5.2. Let R be a prime ring possessing second kind involution with suitable torsion restrictions and L be a Lie ideal of R. Next, let F and G be two Xgeneralized skew derivation with an associated skew derivation of R such that

    [F(r),G(r)]=[r,r],for everyrL.

    Then what we can say about the behaviour of F and G or about the structure of R?

    The characterization of strong commutative preserving (SCP) bgeneralized derivations has been discussed in non-commutative prime rings. In addition, the behavior of bgeneralized derivations with differential/functional identities on prime rings with involution was investigated. Besides, we present some problems for Xgeneralized skew derivations on rings with involution.

    We are very grateful to the referee for his/her appropriate and constructive suggestions which improved the quality of the paper. This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no. (G: 156-662-1439). The authors, therefore, gratefully acknowledge the DSR technical and financial support.

    The authors declare that they have no competing interests.



    [1] V. Aula, Institutions, infrastructures, and data friction—reforming secondary use of health data in Finland, Big Data Soc., 6 (2019), 1–13. http://dx.doi.org/10.1177/2053951719875980 doi: 10.1177/2053951719875980
    [2] European commission, Proposal for a regulation of the European parliament and of the council on the European health data space, European parliament, 2022. Available from: https://www.europarl.europa.eu/legislative-train/theme-promoting-our-european-way-of-life/file-european-health-data-space.
    [3] R. Lun, D. Siegal, T. Ramsay, G. Stotts, D. Dowlatshahi, Synthetic data in cancer and cerebrovascular disease research: a novel approach to big data, PLoS ONE, 19 (2024), e0295921. http://dx.doi.org/10.1371/journal.pone.0295921 doi: 10.1371/journal.pone.0295921
    [4] E. Sizikova, A. Badal, J. G. Delfino, M. Lago, B. Nelson, N. Saharkhiz, et al., Synthetic data in radiological imaging: current state and future outlook, Artif. Intell., 1 (2024), ubae007. http://dx.doi.org/10.1093/bjrai/ubae007 doi: 10.1093/bjrai/ubae007
    [5] J. A. Thomas, R. E. Foraker, N. Zamstein, J. D. Morrow, P. R. Payne, A. B. Wilcox, Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing > 1.8 million SARS-CoV-2 tests in the United States national COVID cohort collaborative (N3C), J. Am. Med. Inform. Asso., 29 (2022), 1350–1365. http://dx.doi.org/10.1093/jamia/ocac045 doi: 10.1093/jamia/ocac045
    [6] H. Murtaza, M. Ahmed, N. F. Khan, G. Murtaza, S. Zafar, A. Bano, Synthetic data generation: state of the art in health care domain, Comput. Sci. Rev., 48 (2023), 100546. http://dx.doi.org/10.1016/j.cosrev.2023.100546 doi: 10.1016/j.cosrev.2023.100546
    [7] A. Gonzales, G. Guruswamy, S. R. Smith, Synthetic data in health care: a narrative review, PLOS Digit Health, 2 (2023), e0000082. http://dx.doi.org/10.1371/journal.pdig.0000082 doi: 10.1371/journal.pdig.0000082
    [8] S. James, C. Harbron, J. Branson, M. Sundler, Synthetic data use: exploring use cases to optimise data utility, Discov. Artif. Intell., 1 (2021), 15. http://dx.doi.org/10.1007/s44163-021-00016-y doi: 10.1007/s44163-021-00016-y
    [9] V. C. Pezoulas, D. I. Zaridis, E. Mylona, C. Androutsos, K. Apostolidis, N. S. Tachos, et al., Synthetic data generation methods in healthcare: a review on open-source tools and methods, Comput. Struct. Biotec., 23 (2024), 2892–2910. http://dx.doi.org/10.1016/j.csbj.2024.07.005 doi: 10.1016/j.csbj.2024.07.005
    [10] C. A. F. López, A. Elbi, On the legal nature of synthetic data, NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research, 2022.
    [11] M. S. Gal, O. Lynskey, Synthetic data: legal implications of the data-generation revolution, Iowa L. Rev., 109 (2023), 1087. http://dx.doi.org/10.2139/ssrn.4414385 doi: 10.2139/ssrn.4414385
    [12] J. Drechsler, A. C. Haensch, 30 years of synthetic data, Statist. Sci., 39 (2024), 221–242. http://dx.doi.org/10.1214/24-STS927
    [13] J. Jordon, L. Szpruch, F. Houssiau, M. Bottarelli, G. Cherubin, C. Maple, et al., Synthetic data—what, why and how? arXiv: 2205.03257. http://dx.doi.org/10.48550/arXiv.2205.03257
    [14] T. E. Raghunathan, J. P. Reiter, D. B. Rubin, Multiple imputation for statistical disclosure limitation, J. Off. Stat., 19 (2003), 1.
    [15] K. El Emam, L. Mosquera, J. Bass, Evaluating identity disclosure risk in fully synthetic health data: model development and validation, J. Med. Internet Res., 22 (2020), 23139. http://dx.doi.org/10.2196/23139 doi: 10.2196/23139
    [16] J. P. Reiter, Inference for partially synthetic, public use microdata sets, Surv. Methodol., 29 (2003), 181–188.
    [17] H. Surendra, H. Mohan, A review of synthetic data generation methods for privacy preserving data publishing, International Journal of Scientific and Technology Research, 6 (2017), 95–101.
    [18] S. Mohiuddin, R. Gardiner, M. Crofts, P. Muir, J. Steer, J. Turner, et al., Modelling patient flows and resource use within a sexual health clinic through discrete event simulation to inform service redesign, BMJ Open, 10 (2020), e037084. http://dx.doi.org/10.1136/bmjopen-2020-037084 doi: 10.1136/bmjopen-2020-037084
    [19] A. A. Tako, K. Kotiadis, C. Vasilakis, A. Miras, C. W. le Roux, Improving patient waiting times: a simulation study of an obesity care service, BMJ Qual. Saf., 23 (2014), 373–381. http://dx.doi.org/10.1136/bmjqs-2013-002107 doi: 10.1136/bmjqs-2013-002107
    [20] J. Yoon, M. Mizrahi, N. F. Ghalaty, T. Jarvinen, A. S. Ravi, P. Brune, et al., EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records, NPJ Digit. Med., 6 (2023), 141. http://dx.doi.org/10.1038/s41746-023-00888-7 doi: 10.1038/s41746-023-00888-7
    [21] L. Juwara, A. El-Hussuna, K. El Emam, An evaluation of synthetic data augmentation for mitigating covariate bias in health data, Patterns, 5 (2024), 100946. http://dx.doi.org/10.1016/j.patter.2024.100946 doi: 10.1016/j.patter.2024.100946
    [22] S. Kaji, S. Kida, Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging, Radiol. Phys. Technol., 12 (2019), 235–248. http://dx.doi.org/10.1007/s12194-019-00520-y doi: 10.1007/s12194-019-00520-y
    [23] S. Dayarathna, K. T. Islam, S. Uribe, G. Yang, M. Hayat, Z. Chen, Deep learning based synthesis of MRI, CT and PET: review and analysis, Med. Image Anal., 92 (2024), 103046. http://dx.doi.org/10.1016/j.media.2023.103046 doi: 10.1016/j.media.2023.103046
    [24] K. Armanious, C. Jiang, M. Fischer, T. Küstner, T. Hepp, K. Nikolaou, et al., MedGAN: medical image translation using GANs, Comput. Med. Imag. Grap., 79 (2020), 101684. http://dx.doi.org/10.1016/j.compmedimag.2019.101684 doi: 10.1016/j.compmedimag.2019.101684
    [25] J. Zhang, X. He, L. Qing, F. Gao, B. Wang, BPGAN: brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer's disease diagnosis, Comput. Meth. Prog. Bio., 217 (2022), 106676. http://dx.doi.org/10.1016/j.cmpb.2022.106676 doi: 10.1016/j.cmpb.2022.106676
    [26] M. J. Tadi, J. Teuho, R. Klén, E. Lehtonen, A. Saraste, C. S. Levin, Synthetic full dose cardiac PET images from low dose scans using conditional GANs, Proceedings of IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2022, 1–2. http://dx.doi.org/10.1109/NSS/MIC44845.2022.10399148
    [27] D. Doncenco, Exploring medical image data augmentation and synthesis using conditional generative adversarial networks, B.S. Thesis, Turku University of Applied Sciences, 2022.
    [28] J. T. Huhtanen, M. Nyman, D. Doncenco, M. Hamedian, D. Kawalya, L. Salminen, et al., Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs, Sci. Rep., 12 (2022), 11803. http://dx.doi.org/10.1038/s41598-022-16154-x doi: 10.1038/s41598-022-16154-x
    [29] P. Movahedi, V. Nieminen, I. M. Perez, H. Daafane, D. Sukhwal, T. Pahikkala et al., Benchmarking evaluation protocols for classifiers trained on differentially private synthetic data, IEEE Access, 12 (2024), 118637–118648. http://dx.doi.org/10.1109/ACCESS.2024.3446913 doi: 10.1109/ACCESS.2024.3446913
    [30] A. R. Benaim, R. Almog, Y. Gorelik, I. Hochberg, L. Nassar, T. Mashiach, et al., Analyzing medical research results based on synthetic data and their relation to real data results: systematic comparison from five observational studies, JMIR Med. Inform., 8 (2020), e16492. http://dx.doi.org/10.2196/16492 doi: 10.2196/16492
    [31] P. Movahedi, V. Nieminen, I. M. Perez, T. Pahikkala, A. Airola, Evaluating classifiers trained on differentially private synthetic health data, Proceedings of IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 2023,748–753. http://dx.doi.org/10.1109/CBMS58004.2023.00313
    [32] B. Nowok, G. M. Raab, C. Dibben, Synthpop: bespoke creation of synthetic data in R, J. Stat. Softw., 74 (2016), 1–26. http://dx.doi.org/10.18637/jss.v074.i11 doi: 10.18637/jss.v074.i11
    [33] A. Montanez, SDV: an open source library for synthetic data generation, Ph.D Thesis, Massachusetts Institute of Technology, 2018.
    [34] T. Li, N. Li, On the tradeoff between privacy and utility in data publishing, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009,517–526. http://dx.doi.org/10.1145/1557019.1557079
    [35] A. Slavković, J. Seeman, Statistical data privacy: a song of privacy and utility, Annu. Rev. Stat. Appl., 10 (2023), 189–218. http://dx.doi.org/10.1146/annurev-statistics-033121-112921 doi: 10.1146/annurev-statistics-033121-112921
    [36] B. Zhao, M. A. Kaafar, N. Kourtellis, Not one but many tradeoffs: privacy vs. utility in differentially private machine learning, Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop, 2020, 15–26. http://dx.doi.org/10.1145/3411495.3421352
    [37] M. Hittmeir, R. Mayer, A. Ekelhart, A baseline for attribute disclosure risk in synthetic data, Proceedings of the Tenth ACM Conference on Data and Application Security and Privacy, 2020,133–143. http://dx.doi.org/10.1145/3374664.3375722
    [38] K. El Emam, L. Mosquera, X. Fang, Validating a membership disclosure metric for synthetic health data, JAMIA Open, 5 (2022), ooac083. http://dx.doi.org/10.1093/jamiaopen/ooac083 doi: 10.1093/jamiaopen/ooac083
    [39] L. Sweeney, Simple demographics often identify people uniquely, Data Privacy Working Paper, 2000.
    [40] L. Sweeney, k-anonymity: a model for protecting privacy, Int. J. Uncertain. Fuzz., 10 (2002), 557–570. http://dx.doi.org/10.1142/S0218488502001648 doi: 10.1142/S0218488502001648
    [41] N. Li, T. Li, S. Venkatasubramanian, t-closeness: privacy beyond k-anonymity and l-diversity, Proceedings of the 23rd international conference on data engineering, 2007,106–115. http://dx.doi.org/10.1109/ICDE.2007.367856
    [42] C. Dwork, A. Roth, The algorithmic foundations of differential privacy, Found. Trends Theor. C., 9 (2014), 211–407. http://dx.doi.org/10.1561/0400000042 doi: 10.1561/0400000042
    [43] M. Finck, F. Pallas, They who must not be identified—distinguishing personal from non-personal data under the GDPR, Int. Data Priv. Law, 10 (2020), 11–36. http://dx.doi.org/10.1093/idpl/ipz026 doi: 10.1093/idpl/ipz026
    [44] A. Cohen, K. Nissim, Towards formalizing the GDPR's notion of singling out, PNAS, 117 (2020), 8344–8352. http://dx.doi.org/10.1073/pnas.1914598117 doi: 10.1073/pnas.1914598117
    [45] M. Veale, R. Binns, L. Edwards, Algorithms that remember: model inversion attacks and data protection law, Phil. Trans. R. Soc. A, 376 (2018), 20180083. http://dx.doi.org/10.1098/rsta.2018.0083 doi: 10.1098/rsta.2018.0083
    [46] C. Sun, J. van Soest, M. Dumontier, Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy, J. Biomed. Inform., 143 (2023), 104404. http://dx.doi.org/10.1016/j.jbi.2023.104404 doi: 10.1016/j.jbi.2023.104404
    [47] J. Jordon, J. Yoon, M. van der Schaar, PATE-GAN: generating synthetic data with differential privacy guarantees, Proceedings of International Conference on Learning Representations, 2019, 1–29.
    [48] N. C. Abay, Y. Zhou, M. Kantarcioglu, B. Thuraisingham, L. Sweeney, Privacy preserving synthetic data release using deep learning, In: Machine learning and knowledge discovery in databases, Cham: Springer, 2019,510–526. http://dx.doi.org/10.1007/978-3-030-10925-7_31
    [49] I. Montoya Perez, P. Movahedi, V. Nieminen, A. Airola, T. Pahikkala, Does differentially private synthetic data lead to synthetic discoveries? Methods Inf. Med., in press. http://dx.doi.org/10.1055/a-2385-1355
    [50] M. I. Khan, M. A. Azeem, E. Alhoniemi, E. Kontio, S. A. Khan, M. Jafaritadi, Regularized weight aggregation in networked federated learning for glioblastoma segmentation, In: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries, Cham: Springer, 2022,121–132. http://dx.doi.org/10.1007/978-3-031-44153-0_12
    [51] M. I. Khan, M. Jafaritadi, E. Alhoniemi, E. Kontio, S. A. Khan, Adaptive weight aggregation in federated learning for brain tumor segmentation, In: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries, Cham: Springer, 2022,455–469. http://dx.doi.org/10.1007/978-3-031-09002-8_40
    [52] M. I. Khan, E. Alhoniemi, E. Kontio, S. A. Khan, M. Jafaritadi, RegAgg: a scalable approach for efficient weight aggregation in federated lesion segmentation of brain MRIs, Proceedings of Eighth International Conference on Fog and Mobile Edge Computing (FMEC), 2023,101–106. http://dx.doi.org/10.1109/FMEC59375.2023.10306171
    [53] J. Xu, B. S. Glicksberg, C. Su, P. Walker, J. Bian, F. Wang, Federated learning for healthcare informatics, J. Healthc. Inform. Res., 5 (2021), 1–19. http://dx.doi.org/10.1007/s41666-020-00082-4 doi: 10.1007/s41666-020-00082-4
    [54] M. Giuffrè, D. L. Shung, Harnessing the power of synthetic data in healthcare: innovation, application, and privacy, NPJ Digit. Med., 6 (2023), 186. http://dx.doi.org/10.1038/s41746-023-00927-3 doi: 10.1038/s41746-023-00927-3
    [55] D. Shanley, J. Hogenboom, F. Lysen, L. Wee, A. Lobo Gomes, A. Dekker, et al., Getting real about synthetic data ethics: are AI ethics principles a good starting point for synthetic data ethics? EMBO Rep., 25 (2024), 2152–2155. http://dx.doi.org/10.1038/s44319-024-00101-0
    [56] B. N. Jacobsen, Machine learning and the politics of synthetic data, Big Data Soc., 10 (2023), 1–12. http://dx.doi.org/10.1177/20539517221145372 doi: 10.1177/20539517221145372
    [57] C. D. Whitney, J. Norman, Real risks of fake data: synthetic data, diversity-washing and consent circumvention, Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024, 1733–1744. http://dx.doi.org/10.1145/3630106.3659002
    [58] G. Ganev, B. Oprisanu, E. De Cristofaro, Robin Hood and Matthew effects: differential privacy has disparate impact on synthetic data, Proceedings of the 39th International Conference on Machine Learning, 2022, 6944–6959.
    [59] T. Hayashi, D. Cimr, H. Fujita, R. Cimler, Interpretable synthetic signals for explainable one-class time-series classification, Eng. Appl. Artif. Intell., 131 (2024), 107716. http://dx.doi.org/10.1016/j.engappai.2023.107716 doi: 10.1016/j.engappai.2023.107716
    [60] J. Vaiste, Ethical implications of AI-generated synthetic health data, HAL Id: hal-04216538.
    [61] J. S. Franklin, K. Bhanot, M. Ghalwash, K. P. Bennett, J. McCusker, D. L. McGuinness, An ontology for fairness metrics, Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022,265–275. http://dx.doi.org/10.1145/3514094.3534137
    [62] K. Bhanot, M. Qi, J. S. Erickson, I. Guyon, K. P. Bennett, The problem of fairness in synthetic healthcare data, Entropy, 23 (2021), 1165. http://dx.doi.org/10.3390/e23091165 doi: 10.3390/e23091165
    [63] T. Farrand, F. Mireshghallah, S. Singh, A. Trask, Neither private nor fair: impact of data imbalance on utility and fairness in differential privacy, Proceedings of the 2020 workshop on privacy-preserving machine learning in practice, 2020, 15–19. http://dx.doi.org/10.1145/3411501.3419419
    [64] V. Volovici, N. L. Syn, A. Ercole, J. J. Zhao, N. Liu, Steps to avoid overuse and misuse of machine learning in clinical research, Nat. Med., 28 (2022), 1996–1999. http://dx.doi.org/10.1038/s41591-022-01961-6 doi: 10.1038/s41591-022-01961-6
    [65] A. S. Hashemi, A. Soliman, J. Lundström, K. Etminani, Domain knowledge-driven generation of synthetic healthcare data, Stud. Health Technol. Inform., 302 (2023), 352–353. http://dx.doi.org/10.3233/SHTI230136 doi: 10.3233/SHTI230136
    [66] J. Latner, M. Neunhoeffer, J. Drechsler, Generating synthetic data is complicated: know your data and know your generator, In: Privacy in statistical databases, Cham: Springer, 2024,115–128. http://dx.doi.org/10.1007/978-3-031-69651-0_8
    [67] F. K. Dankar, M. K. Ibrahim, L. Ismail, A multi-dimensional evaluation of synthetic data generators, IEEE Access, 10 (2022), 11147–11158. http://dx.doi.org/10.1109/ACCESS.2022.3144765 doi: 10.1109/ACCESS.2022.3144765
    [68] M. Miletic, M. Sariyar, Assessing the potentials of LLMs and GANs as state-of-the-art tabular synthetic data generation methods, In: Privacy in statistical databases, Cham: Springer, 2024,374–389. http://dx.doi.org/10.1007/978-3-031-69651-0_25
    [69] R. Hamon, H. Junklewitz, I. Sanchez, Robustness and explainability of artificial intelligence, Luxembourg: Publications Office of the European Union, 2020. http://dx.doi.org/10.2760/57493
    [70] M. Hernandez, G. Epelde, A. Alberdi, R. Cilla, D. Rankin, Synthetic data generation for tabular health records: a systematic review, Neurocomputing, 493 (2022), 28–45. http://dx.doi.org/10.1016/j.neucom.2022.04.053 doi: 10.1016/j.neucom.2022.04.053
    [71] K. Perkonoja, K. Auranen, J. Virta, Methods for generating and evaluating synthetic longitudinal patient data: a systematic review, arXiv: 2309.12380. http://dx.doi.org/10.48550/arXiv.2309.12380
    [72] J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, X. Xiao, PrivBayes: private data release via Bayesian networks, ACM T. Database Syst., 42 (2017), 25. http://dx.doi.org/10.1145/3134428 doi: 10.1145/3134428
    [73] J. de Benedetti, N. Oues, Z. Wang, P. Myles, A. Tucker, Practical lessons from generating synthetic healthcare data with Bayesian networks, In: ECML PKDD 2020 workshops, Cham: Springer, 2020, 38–47. http://dx.doi.org/10.1007/978-3-030-65965-3_3
    [74] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., Generative adversarial nets, Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014, 2672–2680.
    [75] D. P. Kingma, M. Welling, Auto-encoding variational Bayes, arXiv: 1312.6114. http://dx.doi.org/10.48550/arXiv.1312.6114
    [76] C. Yan, Y. Yan, Z. Wan, Z. Zhang, L. Omberg, J. Guinney, et al., A multifaceted benchmarking of synthetic electronic health record generation models, Nat. Commun., 13 (2022), 7609. http://dx.doi.org/10.1038/s41467-022-35295-1 doi: 10.1038/s41467-022-35295-1
    [77] S. Biswal, S. Ghosh, J. Duke, B. Malin, W. Stewart, C. Xiao, J. Sun, EVA: generating longitudinal electronic health records using conditional variational autoencoders, Proceedings of the 6th Machine Learning for Healthcare Conference, 2021,260–282.
    [78] F. K. Dankar, M. Ibrahim, Fake it till you make it: guidelines for effective synthetic data generation, Appl. Sci., 11 (2021), 2158. http://dx.doi.org/10.3390/app11052158 doi: 10.3390/app11052158
    [79] C. Yan, Z. Zhang, S. Nyemba, Z. Li, Generating synthetic electronic health record data using generative adversarial networks: tutorial, JMIR AI, 3 (2024), e52615. http://dx.doi.org/10.2196/52615 doi: 10.2196/52615
    [80] V. Nieminen, T. Pahikkala, A. Airola, Empirical evaluation of amplifying privacy by subsampling for GANs to create differentially private synthetic tabular data, Proceedings of TKTP 2023: Annual Symposium for Computer Science, 2023, 72–81.
    [81] A. Alaa, B. van Breugel, E. S. Saveliev, M. van der Schaar, How faithful is your synthetic data? Sample-level metrics for evaluating and auditing generative models, Proceedings of the 39th International Conference on Machine Learning, 2022,290–306.
    [82] A. Yale, S. Dash, R. Dutta, I. Guyon, A. Pavao, K. P. Bennett, Generation and evaluation of privacy preserving synthetic health data, Neurocomputing, 416 (2020), 244–255. http://dx.doi.org/10.1016/j.neucom.2019.12.136 doi: 10.1016/j.neucom.2019.12.136
    [83] J. Yoon, L. N. Drumright, M. van der Schaar, Anonymization through data synthesis using generative adversarial networks (ADS-GAN), IEEE J. Biomed. Health, 24 (2020), 2378–2388. http://dx.doi.org/10.1109/JBHI.2020.2980262 doi: 10.1109/JBHI.2020.2980262
    [84] V. B. Vallevik, A. Babic, S. E. Marshall, E. Severin, H. M. Brøgger, S. Alagaratnam, et al., Can I trust my fake data—a comprehensive quality assessment framework for synthetic tabular data in healthcare, Int. J. Med. Inform., 185 (2024), 105413. http://dx.doi.org/10.1016/j.ijmedinf.2024.105413 doi: 10.1016/j.ijmedinf.2024.105413
    [85] Z. Azizi, S. Lindner, Y. Shiba, V. Raparelli, C. M. Norris, K. Kublickiene, et al., A comparison of synthetic data generation and federated analysis for enabling international evaluations of cardiovascular health, Sci. Rep., 13 (2023), 11540. http://dx.doi.org/10.1038/s41598-023-38457-3 doi: 10.1038/s41598-023-38457-3
    [86] M. Hernandez, G. Epelde, A. Beristain, R. Álvarez, C. Molina, X. Larrea, et al., Incorporation of synthetic data generation techniques within a controlled data processing workflow in the health and wellbeing domain, Electronics, 11 (2022), 812. http://dx.doi.org/10.3390/electronics11050812 doi: 10.3390/electronics11050812
    [87] C. Little, M. Elliot, R. Allmendinger, Federated learning for generating synthetic data: a scoping review, Int. J. Popul. Data Sci., 8 (2023), 2158. http://dx.doi.org/10.23889/ijpds.v8i1.2158 doi: 10.23889/ijpds.v8i1.2158
    [88] J. W. Kim, B. Jang, Privacy-preserving generation and publication of synthetic trajectory microdata: a comprehensive survey, J. Netw. Comput. Appl., 230 (2024), 103951. http://dx.doi.org/10.1016/j.jnca.2024.103951 doi: 10.1016/j.jnca.2024.103951
    [89] C. Alloza, B. Knox, H. Raad, M. Aguilà, C. Coakley, Z. Mohrova, et al., A case for synthetic data in regulatory decision-making in Europe, Clin. Pharmacol. Ther., 114 (2023), 795–801. http://dx.doi.org/10.1002/cpt.3001 doi: 10.1002/cpt.3001
    [90] A. Beduschi, Synthetic data protection: towards a paradigm change in data regulation? Big Data Soc., 11 (2024), 1–5. http://dx.doi.org/10.1177/20539517241231277
    [91] P. Lehto, S. Malkamäki, The Finnish health sector growth and competitiveness vision 2030, Helsinki: Sitra, 2023.
    [92] Finnish association of private care providers, Sotedigin työkalupakista eväitä tiedon hyödyntämiseen sote-palveluissa, Hyvinvointiala Hali ry, 2023. Available from: https://www.hyvinvointiala.fi/sotedigin-tyokalupakista-evaita-tiedon-hyodyntamiseen-sote-palveluissa/.
    [93] S. Moazemi, T. Adams, H. G. NG, L. Kühnel, J. Schneider, A. F. Näher, et al., NFDI4Health workflow and service for synthetic data generation, assessment and risk management, Stud. Health Technol. Inform., 317 (2024), 21–29. http://dx.doi.org/10.3233/SHTI240834 doi: 10.3233/SHTI240834
  • This article has been cited by:

    1. Orthogonal Generalized Derivation and Left (Resp. Right) Centralizers on Semiprime Ring, 2024, 11, 2394-4099, 108, 10.32628/IJSRSET2411585
    2. Adnan ABBASİ, Abdul KHAN, Mohammad Salahuddin KHAN, Actions of generalized derivations on prime ideals in -rings with applications, 2023, 52, 2651-477X, 1219, 10.15672/hujms.1119353
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1298) PDF downloads(75) Cited by(0)

Figures and Tables

Figures(3)  /  Tables(8)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog