Privacy-aware MMSE estimation

Asoodeh, Shahab; Alajaji, Fady; Linder, Tamás

doi:10.1109/isit.2016.7541647

Cited by 45 publications

(62 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The definition of utility depends on the application scenario. For example, in statistical aggregation, estimation accuracy is often measured by absolute error or mean square error [29] [30]; in location tracking, it is typically measured by Euclidean distance [24]; in privacy-preserving data publishing, distortion is usually used to measure the utility [22].…”

Section: B Privacy and Utility Definitionsmentioning

confidence: 99%

See 1 more Smart Citation

Context-aware Data Aggregation with Localized Information Privacy

Jiang

Tandon

2018

2018 IEEE Conference on Communications and Network Security (CNS)

View full text Add to dashboard Cite

In this paper, localized information privacy (LIP) is proposed, as a new privacy definition, which allows statistical aggregation while protecting users' privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors. We show that LIP relaxes the Localized Differential Privacy (LDP) notion by explicitly modeling the adversary's knowledge. However, it is stricter than 2ǫ-LDP and ǫ-mutual information privacy. The incorporation of local priors allows LIP to achieve higher utility compared to other approaches. For four different applications in privacy-preserving data aggregation, including survey, summation, weighted summation and histogram, we present an optimization framework, with the goal of minimizing the mean square error while satisfying the LIP privacy constraints. Utility-privacy tradeoffs are obtained under each model in closed-form, we then theoretically compare with the centralized information privacy and LDP. At last, we validate our analysis by simulations using both synthetic and real-world data. Results show that our LIP mechanism provides better utility-privacy tradeoffs than LDP and when the prior is not uniformly distributed, the advantage of LIP is even more significant.

show abstract

Section: B Privacy and Utility Definitionsmentioning

confidence: 99%

“…Theorem 3. For the constraint optimization problem defined in (29). The optimal solutions is: q i * mm = 1 − (1 − P i m )/e ǫ , q i * mk = P i k /e ǫ , for all m, k ∈ {1, 2, ..., d}, m = k. Brief steps of proof (detailed proof is shown in Appendix.…”

Section: B Utility-privacy Tradeoff Under Mimo Modelmentioning

confidence: 99%

Context-aware Data Aggregation with Localized Information Privacy

Jiang

Tandon

2018

2018 IEEE Conference on Communications and Network Security (CNS)

View full text Add to dashboard Cite

show abstract

“…They also showed that their adversarial model can be generalized to encompass local DP (wherein the mechanism ensures limited distinction for any pair of entries-a stronger DP notion without a neighborhood constraint [27,56]) [57]. When one restricts the adversary to guessing specific private features (and not all functions of these features), the resulting adversary is a maximum a posteriori (MAP) adversary that has been studied by Asoodeh et al in [52,53,58,59]. Context-aware data perturbation techniques have also been studied in privacy preserving cloud computing [60][61][62].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Generative Adversarial Privacy

Huang

Kairouz

Chen

et al. 2017

Entropy

131

120

View full text Add to dashboard Cite

Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals' private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP's performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model; and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.

show abstract

“…This, in turn, simplifies the PUT to a single-letter optimization for independent and identically distributed (i.i.d.) datasets [10].…”

Section: Introductionmentioning

confidence: 99%

“…We measure utility in terms of a new hard distortion metric, which constrains the privacy mechanism so that the distortion function between original and released datasets is bounded with probability 1. This distortion metric is quite stringent, particularly when compared to average-case distortion constraints [10], but it has the advantage that it allows the data curator to make specific, deterministic guarantees on the fidelity of the disclosed dataset to the original one. This differs significantly from a probabilistic constraint, which does not allow the data This material is based upon work supported by the National Science Foundation under Grant No.…”

Section: Introductionmentioning

confidence: 99%

Privacy Under Hard Distortion Constraints

Liao

Kosut

Sankar

et al. 2018

2018 IEEE Information Theory Workshop (ITW)

View full text Add to dashboard Cite

We study the problem of data disclosure with privacy guarantees, wherein the utility of the disclosed data is ensured via a hard distortion constraint. Unlike average distortion, hard distortion provides a deterministic guarantee of fidelity. For the privacy measure, we use a tunable information leakage measure, namely maximal α-leakage (α ∈ [1, ∞]), and formulate the privacy-utility tradeoff problem. The resulting solution highlights that under a hard distortion constraint, the nature of the solution remains unchanged for both local and nonlocal privacy requirements. More precisely, we show that both the optimal mechanism and the optimal tradeoff are invariant for any α > 1; i.e., the tunable leakage measure only behaves as either of the two extrema, i.e., mutual information for α = 1 and maximal leakage for α = ∞.

show abstract

Privacy-aware MMSE estimation

Cited by 45 publications

References 29 publications

Context-aware Data Aggregation with Localized Information Privacy

Context-aware Data Aggregation with Localized Information Privacy

Context-Aware Generative Adversarial Privacy

Privacy Under Hard Distortion Constraints

Contact Info

Product

Resources

About