Information-Theoretic Privacy Watchdogs

Hsu, Hsiang; Asoodeh, Shahab; Calmon, Flávio P.

doi:10.1109/isit.2019.8849440

Cited by 25 publications

(42 citation statements)

References 11 publications

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“…However, such data also contains sensitive information about individuals or businesses, which makes the privacy regulators, users, and data providers concerned about the leakage of confidential information, either explicitly or implicitly. In signal processing and information theory, data privacy is underpinned in terms of a measure called information lift [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…It is clear that a small lift indicates limited private information gain by the adversary and therefore the more private X is. The lift is the elementary measure in almost all information leakage measures, e.g., mutual information [1,3], Sibson mutual information [4][5][6], α-leakage [7] and local differential privacy [8,9]: as proved in [2], if lift is bounded, all these leakage measures are also bounded.…”

Section: Introductionmentioning

confidence: 99%

“…The existing approach to attain lift-based privacy is the watchdog method proposed in [2]. For a specific privacy constraint, i.e., a threshold ǫ on the lift, the watchdog method filters out and privatizes the high-risk symbols of X.…”

Section: Introductionmentioning

confidence: 99%

“…For a specific privacy constraint, i.e., a threshold ǫ on the lift, the watchdog method filters out and privatizes the high-risk symbols of X. The authors in [2] adopted a uniform approach: merging all highrisk symbols together into a 'super' symbol, which is proved in [6,10] to be the optimal privatization scheme in attaining data privacy. However, this uniform approach neglects an important issue in data privacy: to achieve the benefits of data sharing, the privatized data should provide a satisfactory level of usefulness in X.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Utility in the Watchdog Privacy Mechanism

Zarrabian¹,

Ding²,

Sadeghi³

et al. 2021

Preprint

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This paper is concerned with enhancing data utility in the privacy watchdog method for attaining information-theoretic privacy. For a specific privacy constraint, the watchdog method filters out the high-risk data symbols through applying a uniform data regulation scheme, e.g., merging all high-risk symbols together. While this method entirely trades the symbols resolution off for privacy, we show that the data utility can be greatly improved by partitioning the high-risk symbols set and individually privatizing each subset. We further propose an agglomerative merging algorithm that finds a suitable partition of high-risk symbols: it starts with a singleton high-risk symbol, which is iteratively fused with others until the resulting subsets are private. Numerical simulations demonstrate the efficacy of this algorithm in privately achieving higher utilities in the watchdog scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing Utility in the Watchdog Privacy Mechanism

Zarrabian¹,

Ding²,

Sadeghi³

et al. 2021

Preprint

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“…1 The relationship between information-theoretic DP and local DP (LDP) [5] with other notions of informationtheoretic privacy have been studied. These include conditional mutual information [6] and maximal leakage [7], which are under worst-case source distribution, as well as mutual information [8] and ǫ-log-lift (also known as ǫ-information-privacy or information density) [9]- [11], which assume a given source distribution.…”

Section: Introductionmentioning

confidence: 99%

Differential Privacy for Binary Functions via Randomized Graph Colorings

D’Oliveira¹,

Médard²,

Sadeghi³

2021

Preprint

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We present a framework for designing differentially private (DP) mechanisms for binary functions via a graph representation of datasets. Datasets are nodes in the graph and any two neighboring datasets are connected by an edge. The true binary function we want to approximate assigns a value (or true color) to a dataset. Randomized DP mechanisms are then equivalent to randomized colorings of the graph. A key notion we use is that of the boundary of the graph. Any two neighboring datasets assigned a different true color belong to the boundary.Under this framework, we show that fixing the mechanism behavior at the boundary induces a unique optimal mechanism. Moreover, if the mechanism is to have a homogeneous behavior at the boundary, we present a closed expression for the optimal mechanism, which is obtained by means of a pullback operation on the optimal mechanism of a line graph. For balanced mechanisms, not favoring one binary value over another, the optimal (ǫ, δ)-DP mechanism takes a particularly simple form, depending only on the minimum distance to the boundary, on ǫ, and on δ.

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