We consider the problem of sharing correlated data under a perfect information-theoretic privacy constraint. We focus on redaction (erasure) mechanisms, in which data are either withheld or released unchanged, and measure utility by the average cardinality of the released set, equivalently, the expected Hamming distortion. Assuming the data are generated by a finite time-homogeneous Markov chain, we study the protection of the initial state while maximizing the amount of shared data. We establish a connection between perfect privacy and window-based redaction schemes, showing that erasing data up to a strong stationary time preserves privacy under suitable conditions. We further study an optimal sequential redaction mechanism and prove that it admits an equivalent window interpretation. Interestingly, we show that both mechanisms achieve the optimal distortion while redacting only a constant average number of data points, independent of the data length~$N$.

When users make personal privacy choices, correlation between their data can cause inadvertent leakage about users who do not want to share their data through other users sharing their data. As a solution, we consider local redaction mechanisms. To model pre-existing approaches, we study the class of data-independent privatization mechanisms within this framework and upper-bound their utility when data correlation is modeled by a stationary Markov process. In contrast, we find a novel family of data-dependent mechanisms, which improve the utility by leveraging a data-dependent leakage measure.

In this paper, we investigate the efficient transmisSion of sparse models in a distributed learning system. The system consists of multiple clients, each possessing a sparse local model, and a central server responsible for aggregating the clients' models. Our target is to characterize the tradeoff between communication cost and accuracy in transmissions from the clients to the server. We propose a compression scheme that concatenates a universal covering code and an optimal source code. The numerical results demonstrate an improvement in the communication cost over previous findings in [1]–[3] by comparing with a lower bound on the communication cost derived using a variant of a generalized Fano's inequality.

With the advantages of low latency, low power consumption, and high parallelism, optical neural networks (ONN) offer a promising solution for time-sensitive and resource-limited artificial intelligence applications. However, the performance of the ONN model is often diminished by the gap between the ideal simulated system and the actual physical system. To bridge the gap, this work conducts extensive experiments to investigate systematic errors in the optical physical system within the context of image classification tasks. Through our investigation, two quantifiable errors—light source instability and exposure time mismatches—significantly impact the prediction performance of ONN. To address these systematic errors, a physics-constrained ONN learning framework is constructed, including a well designed loss function to mitigate the effect of light fluctuations, a CCD adjustment strategy to alleviate the effects of exposure time mismatches and a ’physics-prior based’ error compensation network to manage other systematic errors, ensuring consistent light intensity across experimental results and simulations. In our experiments, the proposed method achieved a test classification accuracy of 96.5% on the MNIST dataset, a substantial improvement over the 61.6% achieved with the original ONN. For the more challenging QuickDraw16 and Fashion MNIST datasets, experimental accuracy improved from 63.0% to 85.7% and from 56.2% to 77.5%, respectively. Moreover, the comparison results further demonstrate the effectiveness of the proposed physics-constrained ONN learning framework over state-of-the-art ONN approaches. This lays the groundwork for more robust and precise optical computing applications.

Adaptive video streaming plays a crucial role in ensuring high-quality video streaming services. Despite extensive research efforts devoted to Adaptive BitRate (ABR) techniques, the current reinforcement learning (RL)-based ABR algorithms may benefit the average Quality of Experience (QoE) but suffers from fluctuating performance in individual video sessions. In this paper, we present a novel approach that combines imitation learning with the information bottleneck technique, to learn from the complex offline optimal scenario rather than inefficient exploration. In particular, we leverage the deterministic offline bitrate optimization problem with the future throughput realization as the expert and formulate it as a mixed-integer non-linear programming (MINLP) problem. To enable large-scale training for improved performance, we propose an alternative optimization algorithm that efficiently solves the formulated MINLP problem. To address the overfitting issues due to the future information leakage in MINLP, we incorporate an adversarial information bottleneck framework. By compressing the video streaming state into a latent space, we retain only action-relevant information. Additionally, we introduce a future adversarial term to mitigate the influence of future information leakage, where Model Prediction Control (MPC) policy without any future information is employed as the adverse expert. Experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing the quality of adaptive video streaming, providing a 7.30% average QoE improvement and a 30.01% average ranking reduction.

Semantic communication has emerged as new paradigm shifts in 6G from the conventional syntax-oriented communications. Recently, the wireless broadcast technology has been introduced to support semantic communication system toward higher communication efficiency. Nevertheless, existing broadcast semantic communication systems target on general representation within one stage and fail to balance the inference accuracy among users. In this paper, the broadcast encoding process is decomposed into compression and fusion to improve communication efficiency with adaptation to tasks and channels. Particularly, we propose multiple task-channel-aware sub-encoders (TCEs) and a channel-aware feature fusion sub-encoder (CFE) towards compression and fusion, respectively. In TCEs, multiple local-channel-aware attention blocks are employed to extract and compress task-relevant information for each user. In GFE, we introduce a global-channel-aware fine-tuning block to merge these compressed task-relevant signals into a compact broadcast signal. Notably, we retrieve the bottleneck in DeepBroadcast and leverage information bottleneck theory to further optimize the parameter tuning of TCEs and CFE. We substantiate our approach through experiments on a range of heterogeneous tasks across various channels with additive white Gaussian noise (AWGN) channel, Rayleigh fading channel, and Rician fading channel. Simulation results evidence that the proposed DeepBroadcast outperforms the state-of-the-art methods.

We consider the problem of designing privacy mechanisms for users whose data is correlated based on a Markov random field over a graph, with social networks being a concrete application. Each user, modeled as a node in a graph, has its own personal data and can choose their privacy settings to be: ON or OFF, indicating whether the node requires privacy or not. We suppose that the users’ data is to be shared with a third party, such as electoral or ad campaigns, while respecting the different personal privacy choices of the users.The notion of privacy we use is a variation of differential privacy called dependent differential privacy that can handle the correlated nature of the data. The goal is to preserve the required privacy by releasing a noisy version of each user’s data while minimizing the expected error. We focus on the class of one-hop mechanisms in which each node’s released data depends on its own and its neighbors’ data. This class of mechanisms leads to scalable algorithms that are easily parallelizable. We present One-Hop Algorithm and show that it outputs the privatized data while respecting the privacy settings of each user in the presence of correlation. To give more insight, we consider two examples, star and complete graphs, and compare the privacy-utility tradeoffs of different versions of our algorithm.

We study the problem of intermittent private information retrieval with multiple servers, in which a user consecutively requests one of$K$messages from$N$replicated databases such that part of requests need to be protected while others do not need privacy. Motivated by the location privacy application, the correlation between requests is modeled by a Markov chain. We propose an intermittent private information retrieval scheme that concatenates an obfuscation scheme and a private information retrieval scheme for the time period when privacy is not needed, to prevent leakage incurred by the correlation over time. In the end, we illustrate how the proposed scheme for the problem of intermittent private information retrieval with Markov structure correlation can be applied to design a location privacy protection mechanism in the location privacy problem.

Motivated by the growing availability of personal genomics services, we study an information-theoretic privacy problem that arises when sharing genomic data: a user wants to share his or her genome sequence while keeping the genotypes at certain positions hidden, which could otherwise reveal critical health-related information. A straightforward solution of erasing (masking) the chosen genotypes does not ensure privacy, because the correlation between nearby positions can leak the masked genotypes. We introduce an erasure-based privacy mechanism with perfect information-theoretic privacy, whereby the released sequence is statistically independent of the sensitive genotypes. Our mechanism can be interpreted as a locally-optimal greedy algorithm for a given processing order of sequence positions, where utility is measured by the number of positions released without erasure. We show that finding an optimal order is NP-hard in general and provide an upper bound on the optimal utility. For sequences from hidden Markov models, a standard modeling approach in genetics, we propose an efficient algorithmic implementation of our mechanism with complexity polynomial in sequence length. Moreover, we illustrate the robustness of the mechanism by bounding the privacy leakage from erroneous prior distributions. Our work is a step towards more rigorous control of privacy in genomic data sharing.

We consider the problem of ON-OFF privacy in which a user is interested in the latest message generated by one of n sources available at a server. The user has the choice to turn privacy ON or OFF depending on whether he wants to hide his interest at the time or not. The challenge of allowing the privacy to be toggled between ON and OFF is that the user's online behavior is correlated over time. Therefore, the user cannot simply ignore the privacy requirement when privacy is OFF. We represent the user's correlated requests by an n-state Markov chain. Our goal is to design ON-OFF privacy schemes with optimal download rate that ensure privacy for past and future requests. We devise a polynomial-time algorithm to construct an ON-OFF privacy scheme. Moreover, we present an upper bound on the achievable rate. We show that the proposed scheme is optimal and the upper bound is tight for some special families of Markov chains. We also give an implicit characterization of the optimal achievable rate as a linear programming (LP).

We formulate and study the problem of ON-OFF privacy. ON-OFF privacy algorithms enable a user to continuously switch his privacy between ON and OFF. An obvious example is the incognito mode in internet browsers. But beyond internet browsing, ON-OFF privacy can be a desired feature in most online applications. The challenge is that the statistical correlation over time of a user’s online behavior can lead to leakage of information. We consider the setting in which a user is interested in retrieving the latest message generated by one of$N$sources. The user’s privacy status can change between ON and OFF over time. When privacy is ON the user wants to hide his request. Moreover, since the user’s requests depend on personal attributes such as age, gender, and political views, they are typically correlated over time. As a consequence, the user cannot simply ignore privacy when privacy is OFF. We model the correlation between user’s requests by an$N$state Markov chain. The goal is to design query schemes with optimal download rate, that preserve privacy in an ON-OFF privacy setting. In this paper, we present inner and outer bounds on the achievable download rate for$N$sources. We also devise an efficient algorithm to construct an ON-OFF privacy scheme achieving the inner bound and prove its optimality in the case$N=2$sources. For$N > 2$, finding tighter outer bounds and efficient constructions of ON-OFF privacy schemes that would achieve them remains an open question.

The growing availability of personal genomics services comes with increasing concerns for genomic privacy. Individuals may wish to withhold sensitive genotypes that contain critical health-related information when sharing their data with such services. A straightforward solution that masks only the sensitive genotypes does not ensure privacy due to the correlation structure within the genome. Here, we develop an information-theoretic mechanism for masking sensitive genotypes, which ensures no information about the sensitive genotypes is leaked. We also propose an efficient algorithmic implementation of our mechanism for genomic data governed by hidden Markov models. Our work is a step towards more rigorous control of privacy in genomic data sharing.

The problem of exact-repair regenerating codes against eavesdropping attack is studied. The eavesdropping model we consider is that the eavesdropper has the capability to observe the data involved in the repair of a subset of I nodes. An (n, k, d, I) secure exact-repair regenerating code is an (n, k, d) exact-repair regenerating code that is secure under this eavesdropping model. It has been shown that for some parameters (n, k, d, I), the associated optimal storage-bandwidth tradeoff curve, which has one corner point, can be determined. The focus of this paper is on characterizing such parameters. We establish a lower bound ℓ̂ on the number of wiretap nodes, and show that this bound is tight for the case k = d = n - 1.

We introduce the ON-OFF privacy problem. At each time, the user is interested in the latest message of one of N online sources chosen at random, and his privacy status can be ON or OFF for each request. Only when privacy is ON the user wants to hide the source he is interested in. The problem is to design ON-OFF privacy schemes with maximum download rate that allow the user to obtain privately his requested messages. In many realistic scenarios, the user's requests are correlated since they depend on his personal attributes such as age, gender, political views, or geographical location. Hence, even when privacy is OFF, he cannot simply reveal his request since this will leak information about his requests when privacy was ON. We study the case when the users's requests can be modeled by a Markov chain and N = 2 sources. In this case, we propose an ON-OFF privacy scheme and prove its optimality.

We study the ON-OFF privacy problem. At each time, the user is interested in the latest message of one of N sources. Moreover, the user is assumed to be incentivized to turn privacy ON or OFF whether he/she needs it or not. When privacy is ON, the user wants to keep private which source he/she is interested in. The challenge here is that the user's behavior is correlated over time. Therefore, the user cannot simply ignore privacy when privacy is OFF, because this may leak information about his/her behavior when privacy was ON due to correlation. We model the user's requests by a Markov chain. The goal is to design ON-OFF privacy schemes with optimal download rate that ensure privacy for past and future requests. The user is assumed to know future requests within a window of positive size ω and uses it to construct privacy-preserving queries. In this paper, we construct ON-OFF privacy schemes for N=2 sources and prove their optimality.

The problem of exact-repair regenerating codes against eavesdropping attack is studied. The eavesdropping model we consider is that the eavesdropper has the capability to observe the data involved in the repair of a subset of l nodes. Under this security constraint, it has been shown that the optimal tradeoff curve has a single corner point for some (n, k, d, l). The focus of this paper is on finding parameters (n, k, d, l) whose associated tradeoff curve has this behavior. For k=d=n-1, we prove that the tradeoff curve has a single corner point if and only if l ≥ [[1/4](d-1)]. Previously, it was known that the tradeoff curve has a single corner point if l ≥ [(√d-1)2].

In conventional distributed storage exact repair problems, all sources are reconstructed when the decoder has access to a certain number of encoders (disks). So, the underlying reconstruction network is equivalent to a single-source problem. This paper considers a variant of the exact repair problem, where the underlying reconstruction network is the independent distributed source coding problem, a type of multi-source problem. As the first non-trivial case with two sources and three encoders, the storage-repair tradeoff regions are proved for all the 33 instances, and it is shown that binary codes are optimal.

The problem of characterizing the fundamental tradeoff between storage and repair bandwidth of exact-repair regenerating codes against a passive eavesdropper is studied. The eavesdropper is assumed to be capable of observing the data stored in a fixed number of nodes and the data involved in the repair of these nodes. In this paper, the tradeoff for regenerating codes with small parameters is characterized, and then, the results are extended to some general settings.

The problem of exact-repair regenerating codes against eavesdropping attack is studied. The eavesdropping model we consider is that the eavesdropper has the capability to observe the data involved in the repair of a subset of nodes. In other words, the repair process is required to be secure. The focus of this paper is on such systems with 5 nodes. Specifically, we characterize the rate regions under secure repair for the (5, 3, 4) and (5, 4, 4) instances with 1 or 2 wiretap nodes. While characterizing the rate region of exact-repair regenerating codes remains open, our results indicate that the problem may be more tractable under the security constraint as described.