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The increasing prevalence of massive datasets makes the outsourcing of storage and computation tasks to distributed servers a necessity. This raises a number of concerns regarding the security and integrity of stored information, the privacy of accessing desired information, the communication overhead of distributed systems, the latency, reliability, and complexity of distributed computing, and privacy in distributed training and learning systems. Recent breakthroughs from coding, communication, and information-theoretic perspectives have opened up exciting new research avenues for these topics. There are many theoretical and practical open problems. This Special Issue is dedicated to communication theory, coding theory, information theory, signal processing, and networking aspects of privacy in information retrieval, privacy in coded computing over distributed servers, and privacy in distributed learning. [ABSTRACT FROM AUTHOR]

A fog-aided wireless network architecture is studied in which edge nodes (ENs), such as base stations, are connected to a cloud processor via dedicated fronthaul links while also being endowed with caches. Cloud processing enables the centralized implementation of cooperative transmission strategies at the ENs, albeit at the cost of an increased latency due to fronthaul transfer. In contrast, the proactive caching of popular content at the ENs allows for the low-latency delivery of the cached files, but with generally limited opportunities for cooperative transmission among the ENs. The interplay between cloud processing and edge caching is addressed from an information-theoretic viewpoint by investigating the fundamental limits of a high signal-to-noise-ratio metric, termed normalized delivery time (NDT), which captures the worst case coding latency for delivering any requested content to the users. The NDT is defined under the assumptions of either serial or pipelined fronthaul-edge transmission, and is studied as a function of fronthaul and cache capacity constraints. Placement and delivery strategies across both fronthaul and wireless, or edge, segments are proposed with the aim of minimizing the NDT. Information-theoretic lower bounds on the NDT are also derived. Achievability arguments and lower bounds are leveraged to characterize the minimal NDT in a number of important special cases, including systems with no caching capabilities, as well as to prove that the proposed schemes achieve optimality within a constant multiplicative factor of 2 for all values of the problem parameters. [ABSTRACT FROM PUBLISHER]

The storage of frequently requested multimedia content at small-cell base stations (BSs) can reduce the load of macro-BSs without relying on high-speed backhaul links. In this work, the optimal operation of a system consisting of a cache-aided small-cell BS and a macro-BS is investigated for both offline and online caching settings. In particular, a binary fading one-sided interference channel is considered in which the small-cell BS, whose transmission is interfered by the macro-BS, has a limited-capacity cache. The delivery time per bit (DTB) is adopted as a measure of the coding latency, that is, the duration of the transmission block, required for reliable delivery. For offline caching, assuming a static set of popular contents, the minimum achievable DTB is characterized through information-theoretic achievability and converse arguments as a function of the cache capacity and of the capacity of the backhaul link connecting cloud and small-cell BS. For online caching, under a time-varying set of popular contents, the long-term (average) DTB is evaluated for both proactive and reactive caching policies. Furthermore, a converse argument is developed to characterize the minimum achievable long-term DTB for online caching in terms of the minimum achievable DTB for offline caching. The performance of both online and offline caching is finally compared using numerical results. [ABSTRACT FROM AUTHOR]

Caching at the network edge has emerged as a viable solution for alleviating the severe capacity crunch in modern content centric wireless networks by leveraging network load-balancing in the form of localized content storage and delivery. In this paper, we consider a cache-aided network, where the cache storage phase is assisted by a central server and users can demand multiple files at each transmission interval. To service these demands, we consider two delivery models: 1) centralized content delivery, where user demands at each transmission interval are serviced by the central server via multicast transmissions; and 2) device-to-device assisted distributed delivery, where users multicast to each other in order to service file demands. For such cache-aided networks, we present new results on the fundamental cache storage versus transmission rate tradeoff. Specifically, we develop a new technique for characterizing information theoretic lower bounds on the storage-rate tradeoff and show that the new lower bounds are strictly tighter than cut-set bounds from literature. Furthermore, using the new lower bounds, we improve the constant factor approximation of the optimal storage-rate tradeoff for cache-aided systems under both delivery models. [ABSTRACT FROM PUBLISHER]

Edge caching and the centralization of baseband processing by means of the C-RAN architecture are among the most promising and transformative trends in the evolution of wireless networks. A key advantage of C-RAN is the possibility to perform cooperative transmission across multiple edge nodes, such as small cell base stations, thanks to centralized cloud processing. Cloud processing, however, comes at the cost of the potentially large delay entailed by fronthaul transmission between edge and cloud. In contrast, edge caching enables the low-latency transmission of popular multimedia content, but at the cost of constraining the operation of the edge nodes to decentralized transmission strategies with limited interference management capabilities. In order to accommodate the broad range of quality of service requirements of mobile broadband communication, in terms of spectral efficiency and latency, that are envisioned to be within the scope of 5G systems and beyond, this article considers a hybrid architecture, referred to as fog RAN (F-RAN), that harnesses the benefits of, and the synergies between, edge caching and C-RAN. In an F-RAN, edge nodes may be endowed with caching capabilities, while at the same time being controllable from a central cloud processor as in a C-RAN. In this article, an information-theoretic framework is presented that aims to characterize the main trade-offs between performance of an F-RAN, in terms of worst case delivery latency, and its resources: caching and fronthaul capacities. [ABSTRACT FROM AUTHOR]

Distributed storage systems (DSSs) in the presence of an external wiretapper are considered. A DSS is parameterized by $(n, k, d)$ , in which the data are stored across $n$ nodes (each with storage capacity $\alpha $ ), and must be recoverable by accessing the contents stored on any $k$ out of $n$ nodes. If a node fails, any $d \geq k$ out of $(n-1)$ nodes help in the repair (regeneration) of the failed node (by sending $d\beta $ units of repair data, where $\beta \leq \alpha $ ), so that the data can still be recovered from the DSS. For such a $(n, k, d)$ -DSS, security from the two types of wiretappers is investigated: 1) Type-I (node data) wiretapper, which can read the data stored on any $\ell

Recent results have shown that feedback can significantly increase the capacity of interference networks. This paper considers the impact of noise on such gains due to feedback. In particular, this paper considers the two-user linear deterministic interference channel with noisy feedback, as a stepping stone to characterize the approximate capacity region for the two-user Gaussian interference channel with noisy feedback. First, the capacity region for the symmetric linear deterministic interference channel with noisy feedback is obtained. It is shown that noisy feedback enlarges the capacity region if and only if the number of feedback bits l is greater than a certain threshold l^{*} . It is found that, excluding the regime ({1}/{2}) \leq \alpha \leq 2 , where \alpha is the normalized interference level, in which even full feedback does not increase symmetric capacity, this threshold l^{*} is equal to the per-user symmetric capacity without feedback. One of the key ideas is a novel converse outer bounding technique for the weighted sum rates 2R_{1} + R_{2}$ and R_{1} + 2R_{2} . These results and the techniques developed for the linear deterministic model are then applied to characterize inner bounds and outer bounds for the symmetric Gaussian interference channel with noisy feedback. The outer bounds are shown to be at most 4.7 b/s/Hz away from the achievable rate region. As a corollary, the generalized-degrees-of-freedom region, which approximates the capacity region of the symmetric Gaussian interference channel at high SNR, is found. [ABSTRACT FROM PUBLISHER]

A lossy source coding problem is studied in which a source encoder communicates with two decoders, one with and one without correlated side information with an additional constraint on the privacy of the side information at the uninformed decoder. Two cases of this problem arise depending on the availability of the side information at the encoder. The set of all feasible rate-distortion-equivocation tuples is characterized for each case. The difference between the informed and uninformed cases and the advantages of encoder side information for enhancing privacy are highlighted for a binary symmetric source with erasure side information and Hamming distortion. [ABSTRACT FROM AUTHOR]

We obtain new outer bounds on the capacity regions of the two-user multiple access channel with generalized feedback (MAC-GF) and the two-user interference channel with generalized feedback (IC-GF). These outer bounds are based on the idea of dependence balance which was proposed by Hekstra and Willems. To illustrate the usefulness of our outer bounds, we investigate three different channel models. [ABSTRACT FROM AUTHOR]