Privacy Against Statistical Inference

We propose a general statistical inference framework to capture the privacy threat incurred by a user that releases data to a passive but curious adversary, given utility constraints. We show that applying this general framework to the setting where the adversary uses the self-information cost function naturally leads to a non-asymptotic information-theoretic approach for characterizing the best achievable privacy subject to utility constraints. Based on these results we introduce two privacy metrics, namely average information leakage and maximum information leakage. We prove that under both metrics the resulting design problem of finding the optimal mapping from the user's data to a privacy-preserving output can be cast as a modified rate-distortion problem which, in turn, can be formulated as a convex program. Finally, we compare our framework with differential privacy.Comment: Allerton 2012, 8 page

Privacy-aware variational inference

This thesis focuses on privacy-preserving statistical inference. We use a probabilistic point of view of privacy called differential privacy. Differential privacy ensures that replacing one individual from the dataset with another individual does not affect the results drastically. There are different versions of the differential privacy. This thesis considers the ε-differential privacy also known as the pure differential privacy, and also a relaxation known as the (ε, δ)-differential privacy. We state several important definitions and theorems of DP. The proofs for most of the theorems are given in this thesis. Our goal is to build a general framework for privacy preserving posterior inference. To achieve this we use an approximative approach for posterior inference called variational Bayesian (VB) methods. We build the basic concepts of variational inference with certain detail and show examples on how to apply variational inference. After giving the prerequisites on both DP and VB we state our main result, the differentially private variational inference (DPVI) method. We use a recently proposed doubly stochastic variational inference (DSVI) combined with Gaussian mechanism to build a privacy-preserving method for posterior inference. We give the algorithm definition and explain its parameters. The DPVI method is compared against the state-of-the-art method for DP posterior inference called the differentially private stochastic gradient Langevin dynamics (DP-SGLD). We compare the performance on two different models, the logistic regression model and the Gaussian mixture model. The DPVI method outperforms DP-SGLD in both tasks

Lessons Learned: Defending Against Property Inference Attacks

This work investigates and evaluates multiple defense strategies against property inference attacks (PIAs), a privacy attack against machine learning models. Given a trained machine learning model, PIAs aim to extract statistical properties of its underlying training data, e.g., reveal the ratio of men and women in a medical training data set. While for other privacy attacks like membership inference, a lot of research on defense mechanisms has been published, this is the first work focusing on defending against PIAs. With the primary goal of developing a generic mitigation strategy against white-box PIAs, we propose the novel approach property unlearning. Extensive experiments with property unlearning show that while it is very effective when defending target models against specific adversaries, property unlearning is not able to generalize, i.e., protect against a whole class of PIAs. To investigate the reasons behind this limitation, we present the results of experiments with the explainable AI tool LIME. They show how state-of-the-art property inference adversaries with the same objective focus on different parts of the target model. We further elaborate on this with a follow-up experiment, in which we use the visualization technique t-SNE to exhibit how severely statistical training data properties are manifested in machine learning models. Based on this, we develop the conjecture that post-training techniques like property unlearning might not suffice to provide the desirable generic protection against PIAs. As an alternative, we investigate the effects of simpler training data preprocessing methods like adding Gaussian noise to images of a training data set on the success rate of PIAs. We conclude with a discussion of the different defense approaches, summarize the lessons learned and provide directions for future work

A uniformity-based approach to location privacy

As location-based services emerge, many people feel exposed to high privacy threats. Privacy protection is a major challenge for such services and related applications. A simple approach is perturbation, which adds an artificial noise to positions and returns an obfuscated measurement to the requester. Our main finding is that, unless the noise is chosen properly, these methods do not withstand attacks based on statistical analysis. In this paper, we propose UniLO, an obfuscation operator which offers high assurances on obfuscation uniformity, even in case of imprecise location measurement. We also deal with service differentiation by proposing three UniLO-based obfuscation algorithms that offer multiple contemporaneous levels of privacy. Finally, we experimentally prove the superiority of the proposed algorithms compared to the state-of-the-art solutions, both in terms of utility and resistance against inference attacks

Disparate Vulnerability to Membership Inference Attacks

A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model's training data or not. In this paper, we provide an in-depth study of the phenomenon of disparate vulnerability against MIAs: unequal success rate of MIAs against different population subgroups. We first establish necessary and sufficient conditions for MIAs to be prevented, both on average and for population subgroups, using a notion of distributional generalization. Second, we derive connections of disparate vulnerability to algorithmic fairness and to differential privacy. We show that fairness can only prevent disparate vulnerability against limited classes of adversaries. Differential privacy bounds disparate vulnerability but can significantly reduce the accuracy of the model. We show that estimating disparate vulnerability to MIAs by naïvely applying existing attacks can lead to overestimation. We then establish which attacks are suitable for estimating disparate vulnerability, and provide a statistical framework for doing so reliably. We conduct experiments on synthetic and real-world data finding statistically significant evidence of disparate vulnerability in realistic settings