On Perfect Privacy
On Perfect Privacy
复制标题
论完美隐私
DOI：
10.1109/jsait.2021.3053432
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Rassouli B
中科院分区：
文献类型：
--
作者：
Rassouli B
从信息论的角度对私人数据披露问题进行了研究。考虑一对相依随机变量(X，Y)，其中X和Y分别表示私有数据和有用数据，讨论了以下问题：由互信息I(Y；U)测量的关于Y的最大信息是什么，其中U表示所揭示的数据，而不披露关于X的信息，该信息是由统计独立的条件所捕获的，即X⊥U，以下称为完全隐私)？在输出扰动和全数据观测两种情形下，我们分析了在完全隐私条件下效用的最优性，即I(Y；U)，这两种情形分别对应于马尔可夫核(称为隐私保护映射)应用于Y和(X，Y)对的情况。当X和Y都有一个有限的字母表时，解决方案中涉及的线性代数分析提供了一些有趣的结果，例如释放的字母表大小的上下界和最大效用。然后，证明了对于联合高斯(X，Y)模型，与全数据观测模型相比，在输出扰动模型中不可能有完全隐私。最后，给出了当允许足够小的泄漏时的信息释放率的渐近分析。特别地，在输出扰动模型的背景下，证明了当完全隐私不可行时，该速率总是有限的，并给出了它的两个下界；当完全隐私可行时，证明了在温和的条件下，该速率是无界的。
The problem of private data disclosure is studied from an information theoretic perspective. Considering a pair of dependent random variables (X, Y), where X and Y denote the private and useful data, respectively, the following problem is addressed: What is the maximum information that can be revealed about Y, measured by mutual information I(Y; U), in which U denotes the revealed data, while disclosing no information about X, captured by the condition of statistical independence, i.e., X ⊥ U, and henceforth called perfect privacy)? We analyze the supremization of utility, i.e., I(Y; U) under the condition of perfect privacy for two scenarios: output perturbation and full data observation models, which correspond to the cases where a Markov kernel, called privacy-preserving mapping, applies to Y and the pair (X, Y), respectively. When both X and Y have a finite alphabet, the linear algebraic analysis involved in the solution provides some interesting results, such as upper/lower bounds on the size of the released alphabet and the maximum utility. Afterwards, it is shown that for the jointly Gaussian (X, Y), perfect privacy is not possible in the output perturbation model in contrast to the full data observation model. Finally, an asymptotic analysis is provided to obtain the rate of released information when a sufficiently small leakage is allowed. In particular, in the context of output perturbation model, it is shown that this rate is always finite when perfect privacy is not feasible, and two lower bounds are provided for it; When perfect privacy is feasible, it is shown that under mild conditions, this rate becomes unbounded.
受限数据发布机制下的隐私与效用权衡
DOI：
--
发表时间：
2017
期刊：
arXiv.org
影响因子：
--
作者：
Ye Wang;Y. O. Basciftci;P. Ishwar
通讯作者：
P. Ishwar
在存在替代能源的情况下，多个用户的智能电表隐私
DOI：
10.1109/tifs.2014.2365365
发表时间：
2013
影响因子：
6.8
作者：
J. Gómez;Deniz Gündüz
通讯作者：
Deniz Gündüz
论完美隐私与最大关联
DOI：
--
发表时间：
2017
期刊：
arXiv.org
影响因子：
--
作者：
Borzoo Rassouli;Deniz Gündüz
通讯作者：
Deniz Gündüz