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Register a new userApplication-driven Privacy-preserving Data Publishing with Correlated Attributes
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Added by
Aria Rezaei
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial networks. Given a target application, PR-GAN automatically modifies the data to hide sensitive attributes -- which may be hidden and can be inferred by machine learning algorithms -- while preserving the data utility in the target application. Unlike prior works, the publics possible knowledge of the correlation between the target application and sensitive attributes is built into our modeling. We formulate our problem as an optimization problem, show that an optimal solution exists and use generative adversarial networks (GAN) to create perturbations. We further show that our method provides privacy guarantees under the Pufferfish framework, an elegant generalization of the differential privacy that allows for the modeling of prior knowledge on data and correlations. Through experiments, we show that our method outperforms conventional methods in effectively hiding the sensitive attributes while guaranteeing high performance in the target application, for both property inference and training purposes. Finally, we demonstrate through further experiments that once our model learns a privacy-preserving task, such as hiding subjects identity, on a group of individuals, it can perform the same task on a separate group with minimal performance drops.
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We study anonymization techniques for preserving privacy in the publication of microdata tables. Although existing approaches based on generalization can provide enough protection for identities, anonymized tables always suffer from various attribute disclosures because generalization is inefficient to protect sensitive values and the partition of equivalence groups is directly shown to the adversary. Besides, the generalized table also suffers from serious information loss because the original Quasi-Identifier (QI) values are hardly preserved and the protection against attribute disclosure often causes over-protection against identity disclosure. To this end, we propose a novel technique, called mutual cover, to hinder the adversary from matching the combination of QI values in microdata tables. The rationale is to replace the original QI values with random QI values according to some random output tables that make similar tuples to cover for each other at the minimal cost. As a result, the mutual cover prevents identity disclosure and attribute disclosure more effectively than generalization while retaining the distribution of original QI values as far as possible, and the information utility hardly decreases when enhancing the protection for sensitive values. The effectiveness of mutual cover is verified with extensive experiments.
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Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users. Keeping the sensitive data locally, by running a local agent on the users device, protects the users privacy, however, the agent requires longer to produce useful recommendations, as it does not leverage feedback from other users. This paper proposes a technique we call Privacy-Preserving Bandits (P2B); a system that updates local agents by collecting feedback from other local agents in a differentially-private manner. Comparisons of our proposed approach with a non-private, as well as a fully-private (local) system, show competitive performance on both synthetic benchmarks and real-world data. Specifically, we observed only a decrease of 2.6% and 3.6% in multi-label classification accuracy, and a CTR increase of 0.0025 in online advertising for a privacy budget $epsilon approx 0.693$. These results suggest P2B is an effective approach to challenges arising in on-device privacy-preserving personalization.
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