Optimal Utility-Privacy Trade-off with Total Variation Distance as a Privacy Measure

Rassouli, Borzoo; Gündüz, Deniz

doi:10.1109/itw.2018.8613451

Cited by 33 publications

(45 citation statements)

References 18 publications

(24 reference statements)

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“…Our goal is to make U and X ′ independent of each other. To this end, we minimize the amount of information leakage from U to X ′ [25].…”

Section: Derivation Of the Identity Lossmentioning

confidence: 99%

“…Instead, frameworks based on information theory [17] consider as the measure of privacy the mutual information between the released data and the latent information that can be inferred from data. Under this framework we do not necessarily need to design a noise addition mechanism and we can remove or at least reduce private information while keeping useful service-specific information [25].…”

Section: Introductionmentioning

confidence: 99%

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Mobile sensor data anonymization

Malekzadeh

Clegg

Cavallaro

et al. 2019

Proceedings of the International Conference on Internet of Things Design and Implementation

143

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Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable and wearable devices may reveal private information about users without their awareness. For example, motion data might disclose the weight or gender of a user, or enable their re-identification. To address this problem, we propose an on-device transformation of sensor data to be shared for specific applications, such as monitoring selected daily activities, without revealing information that enables user identification. We formulate the anonymization problem using an information-theoretic approach and propose a new multi-objective loss function for training deep autoencoders. This loss function helps minimizing user-identity information as well as data distortion to preserve the application-specific utility. The training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the output independently of users in the training set. The trained autoencoder can be deployed on a mobile or wearable device to anonymize sensor data even for users who are not included in the training dataset. Data from 24 users transformed by the proposed anonymizing autoencoder lead to a promising trade-off between utility and privacy, with an accuracy for activity recognition above 92% and an accuracy for user identification below 7%.

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“…Our goal is to make U and X ′ independent of each other. To this end, we minimize the amount of information leakage from U to X ′ [25].…”

Section: Derivation Of the Identity Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mobile sensor data anonymization

Malekzadeh

Clegg

Cavallaro

et al. 2019

Proceedings of the International Conference on Internet of Things Design and Implementation

143

View full text Add to dashboard Cite

show abstract

“…Another related problem to the one considered here is the privacy funnel, in which the goal is to reveal the data set X within a given accuracy under some utility measure, while keeping the latent variable W as private as possible [18]. Also, various metrics for quantifying the quality of the disclosure strategy has been studied in [6], [8], [19], [20].…”

Section: B Scenario and Related Workmentioning

confidence: 99%

Data Disclosure Under Perfect Sample Privacy

Rassouli

Rosas

Gündüz

2020

IEEE Trans.Inform.Forensic Secur.

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Perfect data privacy seems to be in fundamental opposition to the economical and scientific opportunities associated with extensive data exchange. Defying this intuition, this paper develops a framework that allows the disclosure of collective properties of datasets without compromising the privacy of individual data samples. We present an algorithm to build an optimal disclosure strategy/mapping, and discuss it fundamental limits on finite and asymptotically large datasets. Furthermore, we present explicit expressions to the asymptotic performance of this scheme in some scenarios, and study cases where our approach attains maximal efficiency. We finally discuss suboptimal schemes to provide sample privacy guarantees to large datasets with a reduced computational cost.

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“…Foremost among them is mutual information (MI): its use as a privacy measure in [15]- [24] is inspired by the common appearance of MI as an operationally-meaningful quantity throughout the literature on communication systems. In a similar vein, divergence-based quantities such as total variation distance between the prior and posterior distributions [25] have also been proposed as leakage measures. Information-theoretic measures have been studied in the DP community via Rényi differential privacy which is based on Rényi divergence [26] that allow relaxing the original definition of DP in order to enable better utility guarantees.…”

Section: Introduction and Overviewmentioning

confidence: 99%

Tunable Measures for Information Leakage and Applications to Privacy-Utility Tradeoffs

Liao

Kosut

Sankar

et al. 2019

IEEE Trans. Inform. Theory

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We introduce a tunable measure for information leakage called maximal α-leakage. This measure quantifies the maximal gain of an adversary in inferring any (potentially random) function of a dataset from a release of the data. The inferential capability of the adversary is, in turn, quantified by a class of adversarial loss functions that we introduce as αloss, α ∈ [1, ∞) ∪ {∞}. The choice of α determines the specific adversarial action and ranges from refining a belief (about any function of the data) for α = 1 to guessing the most likely value for α = ∞ while refining the α th moment of the belief for α in between. Maximal α-leakage then quantifies the adversarial gain under α-loss over all possible functions of the data. In particular, for the extremal values of α = 1 and α = ∞, maximal αleakage simplifies to mutual information and maximal leakage, respectively. For α ∈ (1, ∞) this measure is shown to be the Arimoto channel capacity of order α. We show that maximal αleakage satisfies data processing inequalities and a sub-additivity property thereby allowing for a weak composition result. Building upon these properties, we use maximal α-leakage as the privacy measure and study the problem of data publishing with privacy guarantees, wherein the utility of the released data is ensured via a hard distortion constraint. Unlike average distortion, hard distortion provides a deterministic guarantee of fidelity. We show that under a hard distortion constraint, for α > 1 the optimal mechanism is independent of α, and therefore, the resulting optimal tradeoff is the same for all values of α > 1. Finally, the tunability of maximal α-leakage as a privacy measure is also illustrated for binary data with average Hamming distortion as the utility measure.

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Optimal Utility-Privacy Trade-off with Total Variation Distance as a Privacy Measure

Cited by 33 publications

References 18 publications

Mobile sensor data anonymization

Mobile sensor data anonymization

Data Disclosure Under Perfect Sample Privacy

Tunable Measures for Information Leakage and Applications to Privacy-Utility Tradeoffs

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