In our previous works [1, 2, 3], we defined$(\epsilon ,\delta )$-Local Information Privacy (LIP) as a context-aware privacy notion and presented the corresponding privacy-preserving mechanism. Then we claim that the mechanism satisfies$(\epsilon ,0)$-LIP for any$\epsilon \gt 0$for arbitrary$P_{X}$. However, this claim is not completely correct. In this document, we provide a correction to the valid range of privacy parameters of our previously proposed LIP mechanism. Further, we propose efficient algorithms to expand the range of valid privacy parameters. Finally, we discuss the impact on our experimental results, the rationale of the proposed correction and corrected results. Proofs of the main results in this paper are provided in our full version [6].

Publishing streaming data in a privacy-preserving manner has been a key research focus for many years. This issue presents considerable challenges, particularly due to the correlations prevalent within the data stream. Existing approaches either fall short in effectively leveraging these correlations, leading to a suboptimal utility-privacy tradeoff, or they involve complex mechanism designs that increase the computation complexity with respect to the sequence length. In this paper, we introduce Sequence Information Privacy (SIP), a new privacy notion designed to guarantee privacy for an entire data stream, taking into account the intrinsic data correlations. We show that SIP provides a similar level of privacy guarantee compared to local differential privacy (LDP), and it also enjoys a lightweight modular mechanism design. We further study two online data release models (instantaneous or batched) and propose corresponding privacy-preserving data perturbation mechanisms. We provide a numerical evaluation of how correlations influence noise addition in data streams. Lastly, we conduct experiments using real-world data to compare the utility-privacy tradeoff offered by our approaches with those from existing literature. The results reveal that our mechanisms achieve better utility-privacy tradeoff than the state-of-the-art LDP-based mechanisms. Notably, the improvements become more significant for small privacy budgets.

In this paper, we consider the problem of answering count queries for genomic data subject to perfect privacy constraints. Count queries are often used in applications that collect aggregate (population-wide) information from biomedical Databases (DBs) for analysis, such as Genome-wide association studies. Our goal is to design mechanisms for answering count queries of the following form:How many users in the database have a specific set of genotypes at certain locations in their genome?At the same time, we aim to achieve perfect privacy (zero information leakage) of the sensitive genotypes at a pre-specified set of secret locations. The sensitive genotypes could indicate rare diseases and/or other health traits one may want to keep private. We present both local and central count-query mechanisms for the above problem that achieves perfect information-theoretic privacy for sensitive genotypes while minimizing the expected absolute error (or per-user error probability, depending on the setting) of the query answer. We also derived a lower bound of the per-user probability of error for an arbitrary query-answering mechanism that satisfies perfect privacy. We show that our mechanisms achieve error close to the lower bound, and match the lower bound for some special cases. We numerically show that the performance of each mechanism depends on the data prior distribution, the intersection between the queried and sensitive genotypes, and the strength of the correlation in the genomic data sequence.

In this article, we propose local information privacy (LIP), and design LIP based mechanisms for statistical aggregation while protecting users’ privacy without relying on a trusted third party. The concept of context-awareness is incorporated in LIP, which can be viewed as exploiting of data prior (both in privatizing and post-processing) to enhance data utility. We present an optimization framework to minimize the mean square error of data aggregation while protecting the privacy of each user’s input data or a correlated latent variable by satisfying LIP constraints. Then, we study optimal mechanisms under different scenarios considering the prior uncertainty and correlation with a latent variable. Three types of mechanisms are studied in this article, including randomized response (RR), unary encoding (UE), and local hashing (LH), and we derive closed-form solutions for the optimal perturbation parameters that are prior-dependent. We compare LIP-based mechanisms with those based on LDP, and theoretically show that the former achieve enhanced utility. We then study two applications: (weighted) summation and histogram estimation, and show how proposed mechanisms can be applied to each application. Finally, we validate our analysis by simulations using both synthetic and real-world data. Results show the impact on data utility by different prior distributions, correlations, and input domain sizes. Results also show that our LIP-based mechanisms provide better utility-privacy tradeoffs than LDP-based ones.

It is crucial to protect users’ location traces against inference attacks on aggregate mobility data collected from multiple users in various real-world applications. Most of the existing works on aggregate mobility data are focusing on inference attacks rather than designing privacy-preserving release mechanisms, and a few differential private release mechanisms suffer from poor utility-privacy tradeoffs. In this paper, we propose optimal centralized privacy-preserving aggregate mobility data release mechanisms (PAMDRMs) that minimize the leakage from an information-theoretic perspective by releasing perturbed versions of the raw aggregate location. Specifically, we use mutual information to measure user-level and aggregate-level privacy leakage separately, and formulate leakage minimization problems under utility constraints. As directly solving the optimization problems incur exponential complexity w.r.t. users’ trace length, we transform them into belief state Markov Decision Processes (MDPs), with a focus on the MDP formulation for the user-level privacy problem. We build reinforcement learning (RL) models and leverage the efficient Asynchronous Advantage Actor-Critic RL algorithm to derive the solutions to the MDPs as our optimal PAMDRMs. We compare them with two state-of-the-art privacy protection mechanisms PDPR (context-aware local design) and DMLM (context-free centralized design) in terms of mutual information leakage and adversary’s attack success (evaluated by her expected estimation error and Jensen-Shannon Divergence-based error). Extensive experimental results on both synthetic and real-world datasets demonstrate that the user-level PAMDRM performs the best on both measures thanks to its context-aware property and centralized design. Even though the aggregate-level PAMDRM achieves better privacy-utility tradeoff than the other two, it does not always perform better than them on adversarial success, highlighting the necessity of considering privacy measures from different perspectives to avoid overestimating the level of privacy offered to users. Lastly, we discuss an alternative, fully data-driven approach to derive the optimal PAMDRM by leveraging adversarial training on limited data samples.

In this paper, we study Local Information Privacy (LIP). As a context-aware privacy notion, LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by incorporating prior knowledge and therefore achieving better utility. We study the relationships between LIP and some of the representative privacy notions including LDP, mutual information and maximal leakage. We show that LIP provides strong instance-wise privacy protection compared to other context-aware privacy notions. Moreover, we present some useful properties of LIP, including post-processing, linkage, composability, transferability and robustness to imperfect prior knowledge. Then we study a general utility-privacy tradeoff framework, under which we derive LIP based privacy-preserving mechanisms for both discrete and continuous-valued data. Three types of perturbation mechanisms are studied in this paper: 1) randomized response (RR), 2) random sampling (RS) and 3) additive noise (AN) (e.g., Gaussian mechanism). Our privacy mechanisms incorporate the prior knowledge into the perturbation parameters so as to enhance utility. Finally, we present a comprehensive set of experiments on real datasets to illustrate the advantage of context-awareness and compare the utility-privacy tradeoffs provided by different mechanisms.

In this paper, we analyze the regret bound of Multi-armed Bandit (MAB) algorithms under the setting where the payoffs of an arbitrary-size cluster of arms are observable in each round. Compared to the well-studied bandit or full feedback setting, where the payoffs of the selected arm or all the arms are observable, the clustered feedback setting can be viewed as a generalization and a connection. We focus on two most representative MAB algorithms: Upper Confidence Bound and Thompson sampling, and adapt them into the clustered feedback setting. Then, we theoretically derive the regret bound for each of them considering the general type of payoffs (value comes from continuous domains). We show that the regret bounds of these two algorithms with clustered information feedback depend only on the number of clusters. Finally, we simulate both synthetic data and real-world data to compare the performance of these algorithms with different numbers of observable payoffs in each round, the results validate our analysis.

A localized privacy protection notion: local information privacy (LIP) is studied in this paper. As a context-aware notion that considers prior knowledge, the LIP notion is shown to provide increased utility than local differential privacy (LDP). Within the scope of LIP, we further consider scenarios with uncertainty on the prior knowledge, i.e., the prior is bounded within a certain range or the prior is arbitrary. The former case is defined as bounded-prior LIP (BP-LIP), and the latter as worst-case LIP (WC-LIP). The contributions of this paper are three-fold: We first provide theoretical results which show the connections of these new definitions with LDP; Secondly, we present an optimization framework for privacy-preserving data collection, with the goal of minimizing the expected squared error while satisfying BP-LIP and WC-LIP privacy constraints. Utility-privacy tradeoffs are obtained in closed-form. At last, we validate our conclusions by numerical analysis and real-world data simulation. Our results show that the notion of bounded-prior LIP can achieve better utility-privacy tradeoff compared to context free notion of LDP.