Empirical inference attacks are a popular approach for evaluating the privacy risk of data release mechanisms in practice. While an active attack literature exists to evaluate machine learning models or synthetic data release, we currently lack comparable methods for fixed aggregate statistics, in particular when only a limited number of statistics are released. We here propose an inference attack framework against fixed aggregate statistics and an attribute inference attack called DeSIA (Deterministic-Stochastic Inference Attack). DeSIA consists of two modules: a deterministic module, which verifies whether the target user's attribute can be uniquely inferred, and a stochastic module, which estimates the most likely attribute value when it cannot be uniquely inferred. We instantiate DeSIA against the U.S. Census PPMF dataset and show it to identify risks missed by reconstruction-based attacks. In particular, we show DeSIA to be highly effective in identifying vulnerable users, achieving a true positive rate of 0.14 at a false positive rate of $10^{-3}$. We show DeSIA to outperform all reconstruction-based attacks even without access to a real-world auxiliary dataset. We show DeSIA to be robust to varying levels of noise addition and across varying numbers of released aggregate statistics. We perform an extensive ablation study of DeSIA and show how DeSIA can be successfully adapted to the membership inference task. Overall, our results show that (a) even a limited number of aggregate statistics can reveal sufficient information to expose users to risk from inference attacks, leaving some users disproportionately vulnerable, and (b) emphasize the need for formal privacy mechanisms and testing before aggregate statistics are released.

Query-based systems (QBSs) are one of the key approaches for sharing data. QBSs allow analysts to request aggregate information from a private protected dataset. Attacks are a crucial part of ensuring QBSs are truly privacy-preserving. The development and testing of attacks is however very labor-intensive and unable to cope with the increasing complexity of systems. Automated approaches have been shown to be promising but are currently extremely computationally intensive, limiting their applicability in practice. We here propose QueryCheetah, a fast and effective method for automated discovery of privacy attacks against QBSs. We instantiate QueryCheetah on attribute inference attacks and show it to discover stronger attacks than previous methods while being 18 times faster than the state-of-the-art automated approach. We then show how QueryCheetah allows system developers to thoroughly evaluate the privacy risk, including for various attacker strengths and target individuals. We finally show how QueryCheetah can be used out-of-the-box to find attacks in larger syntaxes and workarounds around ad-hoc defenses.

Abstract An essential characteristic of data streams is the possibility of occurrence of concept drift, i.e., change in the distribution of the data in the stream over time. The capability to detect and adapt to changes in data stream mining methods is thus a necessity. While methods for multi-target prediction on data streams have recently appeared, they have largely remained without such capability. In this paper, we propose novel methods for change detection and adaptation in the context of incremental online learning of decision trees for multi-target regression. One of the approaches we propose is ensemble based, while the other uses the Page–Hinckley test. We perform an extensive evaluation of the proposed methods on real-world and artificial data streams and show their effectiveness. We also demonstrate their utility on a case study from spacecraft operations, where cosmic events can cause change and demand an appropriate and timely positioning of the space craft.