We consider a distributed empirical risk minimization (ERM) optimization problem with communication efficiency and privacy requirements, motivated by the federated learning (FL) framework (Kairouz et al., 2019).
The graph matching problem emerges naturally in various applications such as Web privacy, image processing and computational biology. In this article, graph matching is considered under a stochastic model, where a pair of randomly generated graphs with pairwise correlated edges are to be matched such that given the labeling of the vertices in the first graph, the labels in the second graph are recovered by leveraging the correlation among their edges. The problem is considered under various settings and graph models.
Motivated by applications to covert quantum radar, we analyze a covert quantum sensing problem, in which a legitimate user aims at estimating an unknown parameter taking finitely many values by probing a quantum channel while remaining undetectable from an adversary receiving the probing signals through another quantum channel. When channels are classical-quantum, we characterize the optimal error exponent under a covertness constraint for sensing strategies in which probing signals do not depend on past observations.
The relationships among secrecy, compression rate and shared secret key rate in lossless data compression are studied through the lenses of perfect secrecy, mutual information leakage, maximal leakage, local differential privacy, and secrecy by design. It is revealed that the utility cost of jointly compressing and securing data is very sensitive to the adopted secrecy metric and the specifics of the compression setting.
The growing size of modern datasets necessitates splitting a large scale computation into smaller computations and operate in a distributed manner. Adversaries in a distributed system deliberately send erroneous data in order to affect the computation for their benefit. Boolean functions are the key components of many applications, e.g., verification functions in blockchain systems and design of cryptographic algorithms.
Cloud computing platforms have created the possibility for computationally limited users to delegate demanding tasks to strong but untrusted servers. Verifiable computing algorithms help build trust in such interactions by enabling the server to provide a proof of correctness of his results which the user can check very efficiently. In this article, we present a doubly-efficient interactive algorithm for verifiable polynomial evaluation.
Establishing code equivalences between index coding and network coding provides important insights for code design. Previous works showed an equivalence relation between any index-coding instance and a network-coding instance, for which a code for one instance can be translated to a code for the other instance with the same decoding-error performance. The equivalence also showed a surprising result that any network-coding instance can be mapped to an index-coding instance with a properly designed code translation.
We consider three different variants of differential privacy (DP), namely approximate DP, Rényi DP (RDP), and hypothesis test DP. In the first part, we develop a machinery for optimally relating approximate DP to RDP based on the joint range of two $f$ -divergences that underlie the approximate DP and RDP. In particular, this enables us to derive the optimal approximate DP parameters of a mechanism that satisfies a given level of RDP.
Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users. In particular, the overhead of the state-of-the-art protocols for secure model aggregation grows quadratically with the number of users.
The problem of private data disclosure is studied from an information theoretic perspective. Considering a pair of dependent random variables (X, Y), where X and Y denote the private and useful data, respectively, the following problem is addressed: What is the maximum information that can be revealed about Y, measured by mutual information I(Y; U), in which U denotes the revealed data, while disclosing no information about X, captured by the condition of statistical independence, i.e., X ⊥ U, and henceforth called perfect privacy)?