Data Disclosure Under Perfect Sample Privacy

Rassouli, Borzoo; Rosas, Fernando; Gündüz, Deniz

doi:10.1109/tifs.2019.2954652

Cited by 27 publications

(19 citation statements)

References 34 publications

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“…Efficient algorithms to compute these quantities are discussed in Ref. [ 26 ]. Although current implementations allow only relatively small systems, this line of thinking shows that future advances in PID might make the computation of emergence indices more scalable, avoiding the limitations of Eq (10) .…”

Section: Measuring Emergencementioning

confidence: 99%

Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data

et al. 2020

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The broad concept of emergence is instrumental in various of the most challenging open scientific questions—yet, few quantitative theories of what constitutes emergent phenomena have been proposed. This article introduces a formal theory of causal emergence in multivariate systems, which studies the relationship between the dynamics of parts of a system and macroscopic features of interest. Our theory provides a quantitative definition of downward causation, and introduces a complementary modality of emergent behaviour—which we refer to as causal decoupling. Moreover, the theory allows practical criteria that can be efficiently calculated in large systems, making our framework applicable in a range of scenarios of practical interest. We illustrate our findings in a number of case studies, including Conway’s Game of Life, Reynolds’ flocking model, and neural activity as measured by electrocorticography.

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Section: Measuring Emergencementioning

confidence: 99%

Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data

et al. 2020

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“…They showed that such a random variable Z exists if and only if the columns of P S|X are linearly dependent. Inspired by this notion of weakly independent, the authors in [17], [24] carefully studied and analyzed perfect obfuscation problem where the goal is to release the useful information X while keeping S as private. Here we extend both, and establish the notion of weakly dependence based on KL-divergence and χ 2 -divergence.…”

Section: Local Information Geometry Analysismentioning

confidence: 99%

On Perfect Obfuscation: Local Information Geometry Analysis

Razeghi

Calmon

Gündüz

et al. 2020

2020 IEEE International Workshop on Information Forensics and Security (WIFS)

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We consider the problem of privacy-preserving data release for a specific utility task under perfect obfuscation constraint. We establish the necessary and sufficient condition to extract features of the original data that carry as much information about a utility attribute as possible, while not revealing any information about the sensitive attribute. This problem formulation generalizes both the information bottleneck and privacy funnel problems. We adopt a local information geometry analysis that provides useful insight into information coupling and trajectory construction of spherical perturbation of probability mass functions. This analysis allows us to construct the modal decomposition of the joint distributions, divergence transfer matrices, and mutual information. By decomposing the mutual information into orthogonal modes, we obtain the locally sufficient statistics for inferences about the utility attribute, while satisfying perfect obfuscation constraint. Furthermore, we develop the notion of perfect obfuscation based on χ 2 -divergence and Kullback-Leibler divergence in the Euclidean information space.

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“…The framework of relaxed notions of privacy (including latent-variable privacy) has been explored in various other problems. For instance, within the context of privacy preserving data release, several works [13], [14] have proposed new privacy definitions and mechanisms with bounded leakage for latent (secret) attributes. The problem of latent-variable private information retrieval (PIR) was recently introduced in [15], where the goal is to retrieve content while satisfying perfect privacy for latent attributes.…”

Section: Introductionmentioning

confidence: 99%

Wiretap Channel with Latent Variable Secrecy

Mutangana

Tandon

Goldfeld

et al. 2021

2021 IEEE International Symposium on Information Theory (ISIT)

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The classic wiretap channel (WTC) problem is concerned with a transmitter (Alice) that wants to send a message W to the intended receiver (Bob) while keeping it secret from a passive eavesdropper (Eve). However, under certain communication scenarios, the user may not be interested in hiding the entire message from the eavesdropper, but rather in hiding its most sensitive attributes. While classic wiretap coding is capable of hiding these salient message attributes, it may be too stringent and better communication rates may be achievable.Motivated by the above, in this paper, we introduce and study the latent variable wiretap channel (LV-WTC) problem. Under this setting, the transmitter is interested in sending the message W to the intended receiver while keeping a correlated latent variable S (which models privacy sensitive attributes) secret from the eavesdropper. We present a message splitting based achievable scheme for the LV-WTC problem, which adapts to the structure of the conditional distribution P S|W to achieve higher rates compared to the classical WTC. Several open problems and future directions that originate from this new communication problem are also discussed.

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Data Disclosure Under Perfect Sample Privacy

Cited by 27 publications

References 34 publications

Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data

Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data

On Perfect Obfuscation: Local Information Geometry Analysis

Wiretap Channel with Latent Variable Secrecy

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