204 results found
Li Z, Oechtering TJ, Gunduz D, 2019, Privacy Against a Hypothesis Testing Adversary, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, Vol: 14, Pages: 1567-1581, ISSN: 1556-6013
Tao M, Gunduz D, Xu F, et al., Content Caching and Delivery in Wireless Radio Access Networks, IEEE Transactions on Communications, ISSN: 0090-6778
Rassouliy B, Gunduz D, 2019, Optimal utility-privacy trade-off with total variation distance as a privacy measure, IEEE Transactions on Information Forensics and Security, ISSN: 1556-6013
The total variation distance is proposed as a privacy measure in an information disclosure scenario when the goal is to reveal some information about available data in return of utility, while retaining the privacy of certain sensitive latent variables from the legitimate receiver. The total variation distance is introduced as a measure of privacy-leakage by showing that: i) it satis?es the post-processing and linkage inequalities, which makes it consistent with an intuitive notion of a privacy measure; ii) the optimal utility-privacy trade-off can be solved through a standard linear program when total variation distance is employed as the privacy measure; iii) it provides a bound on the privacy-leakage measured by mutual information, maximal leakage, or the improvement in an inference attack with a bounded cost function.
Ceran ET, Gunduz D, Gyorgy A, 2019, Average Age of Information With Hybrid ARQ Under a Resource Constraint, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, Vol: 18, Pages: 1900-1913, ISSN: 1536-1276
Mohammadi Amiri M, Gunduz D, Computation scheduling for distributed machine learning with straggling workers, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: Institute of Electrical and Electronics Engineers
We study scheduling of computation tasks acrossnworkers in a large scale distributed learning problem. Computa-tion speeds of the workers are assumed to be heterogeneous andunknown to the master, and redundant computations are assignedto the workers in order to tolerate straggling workers. We con-sider sequential computation and instantaneous communicationfrom each worker to the master, and each computation round,which can model a single iteration of the stochastic gradientdescent (SGD) algorithm, iscompletedonce the master receivesk≤ndistinct computations, referred to as thecomputationtarget. Our goal is to characterize theaverage completion timeas a function of thecomputation load, which denotes the portionof the dataset available at each worker, and the computationtarget. We propose two computation scheduling schemes thatspecify the computation tasks assigned to each worker, as wellas their order of execution. We also establish a lower bound onthe minimum average completion time. Numerical results showa significant reduction in the average computation time over theexisting coded and uncoded computing schemes.
Sreekumar S, Gündüz D, Cohen A, 2019, Distributed hypothesis testing under privacy constraints
© 2018 IEEE Information Theory Workshop, ITW 2018. All rights reserved. A distributed binary hypothesis testing problem involving two parties, a remote observer and a detector, is studied. The remote observer has access to a discrete memoryless source, and communicates its observations to the detector via a rate-limited noiseless channel. The detector tests for the independence of its own observations with that of the observer, conditioned on some additional side information. While the goal is to maximize the type 2 error exponent of the test for a given type 1 error probability constraint, it is also desired to keep a private part, which is correlated with the observer's observations, as oblivious to the detector as possible. Considering equivocation and average distortion as the metrics of privacy at the detector, a tight single-letter characterization of the rate-error exponent-equivocation and rate-error exponent-distortion tradeoff is obtained.
Rassouli B, Gündüz D, 2019, Optimal utility-privacy trade-off with total variation distance as a privacy measure, 2018 IEEE Information Theory Workshop, ITW 2018
© 2018 IEEE Information Theory Workshop, ITW 2018. All rights reserved. The total variation distance is proposed as a privacy measure in an information disclosure scenario when the goal is to reveal some information about available data in order to receive utility, while preserving the privacy of sensitive data from the legitimate receiver. The total variation distance is motivated as a measure of privacy-leakage by showing that: i) it satisfies the post-processing and linkage inequalities, which makes it consistent with an intuitive notion of a privacy measure; ii) the optimal utility-privacy trade-off can be solved through a standard linear program when total variation distance is employed as the privacy measure; iii) it provides a bound on the privacy-leakage measured by mutual information, maximal leakage, or the improvement in an inference attack with an arbitrary bounded cost function.
Mital N, Kralevska K, Ling C, et al., 2019, Storage-repair bandwidth trade-off for wireless caching with partial failure and broadcast repair
© 2018 IEEE Information Theory Workshop, ITW 2018. All rights reserved. Repair of multiple partially failed cache nodes is studied in a distributed wireless content caching system, where r out of a total of n cache nodes lose part of their cached data. Broadcast repair of failed cache contents at the network edge is studied; that is, the surviving cache nodes transmit broadcast messages to the failed ones, which are then used, together with the surviving data in their local cache memories, to recover the lost content. The trade-off between the storage capacity and the repair bandwidth is derived. It is shown that utilizing the broadcast nature of the wireless medium and the surviving cache contents at partially failed nodes significantly reduces the required repair bandwidth per node.
Tung T-Y, Gunduz D, 2018, SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency-Domain Sparsity, IEEE COMMUNICATIONS LETTERS, Vol: 22, Pages: 2451-2454, ISSN: 1089-7798
Giaconi G, Gunduz D, Poor HV, 2018, Privacy-Aware Smart Metering Progress and challenges, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 35, Pages: 59-78, ISSN: 1053-5888
Somuyiwa SO, Gunduz D, Gÿorgy A, 2018, Reinforcement Learning for Proactive Caching of Contents with Different Demand Probabilities, ISSN: 2154-0217
© 2018 IEEE. A mobile user randomly accessing a dynamic content library over a wireless channel is considered. At each time instant, a random number of contents are added to the library and each content remains relevant to the user for a random period of time. Contents are classified into finitely many classes such that whenever the user accesses the system, he requests each content randomly with a class-specific demand probability. Contents are downloaded to the user equipment (UE) through a wireless link whose quality also varies randomly with time. The UE has a cache memory of finite capacity, which can be used to proactively store contents before they are requested by the user. Any time contents are downloaded, the system incurs a cost (energy, bandwidth, etc.) that depends on the channel state at the time of download, and scales linearly with the number of contents downloaded. Our goal is to minimize the expected long-term average cost. The problem is modeled as a Markov decision process, and the optimal policy is shown to exhibit a threshold structure; however, since finding the optimal policy is computationally infeasible, parametric approximations to the optimal policy are considered, whose parameters are optimized using the policy gradient method. Numerical simulations show that the performance gain of the resulting scheme over traditional reactive content delivery is significant, and increases with the cache capacity. Comparisons with two performance lower bounds, one computed based on infinite cache capacity and another based on non-casual knowledge of the user access times and content requests, demonstrate that our scheme can perform close to the theoretical optimum.
Rassouli B, Varasteh M, Gunduz D, 2018, Gaussian multiple access channels with one-bit quantizer at the receiver, Entropy, Vol: 20, ISSN: 1099-4300
The capacity region of a two-transmitter Gaussian multiple access channel(MAC) under average input power constraints is studied, when the receiveremploys a zero-threshold one-bit analog-to-digital converter (ADC). It isproved that the input distributions of the two transmitters that achieve theboundary points of the capacity region are discrete. Based on the position of aboundary point, upper bounds on the number of the mass points of thecorresponding distributions are derived.
Guler B, Gunduz D, Yener A, 2018, Lossy Coding of Correlated Sources Over a Multiple Access Channel: Necessary Conditions and Separation Results, IEEE TRANSACTIONS ON INFORMATION THEORY, Vol: 64, Pages: 6081-6097, ISSN: 0018-9448
Shi J, Liu L, Gunduz D, et al., 2018, Polar Codes and Polar Lattices for the Heegard-Berger Problem, IEEE TRANSACTIONS ON COMMUNICATIONS, Vol: 66, Pages: 3760-3771, ISSN: 0090-6778
Rassouli B, Gunduz D, 2018, On Perfect Privacy, Pages: 2551-2555, ISSN: 2157-8095
© 2018 IEEE. For a pair of (dependent) random variables (X, Y), the following problem is addressed: What is the maximum information that can be revealed about Y, while disclosing no information about X? Assuming that a Markov kernel maps Y to the revealed information U, it is shown that the maximum mutual information between Y and U, i.e., I(Y; U), can be obtained as the solution of a standard linear program, when X and U are required to be independent, called perfect privacy. The resulting quantity is shown to be greater than or equal to the non-private information about X carried by Y. For jointly Gaussian (X, Y), it is shown that perfect privacy is not possible if the kernel is applied to only Y; whereas perfect privacy can be achieved if the mapping is from both X and Y; that is, if the private variables can also be observed at the encoder. Finally, it is shown that when Y is not a deterministic function of X, perfect privacy is always feasible when the mapping has access to both X and Y.1
Amiri MM, Gunduz D, 2018, Caching and Coded Delivery Over Gaussian Broadcast Channels for Energy Efficiency, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 36, Pages: 1706-1720, ISSN: 0733-8716
Ceran ET, Gunduz D, Gyorgy A, 2018, Average age of information with hybrid ARQ under a resource constraint, Wireless Communications and Networking Conference (WCNC), Publisher: IEEE, ISSN: 1525-3511
Scheduling the transmission of status updates over an error-prone communication channel is studied in order to minimize the long-term average age of information (AoI) at the destination under a constraint on the average number of transmissions at the source node. After each transmission, the source receives an instantaneous ACK/NACK feedback, and decides on the next update without prior knowledge on the success of future transmissions. First, the optimal scheduling policy is studied under different feedback mechanisms when the channel statistics are known; in particular, the standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols are considered. Then, for an unknown environment, an average-cost reinforcement learning (RL) algorithm is proposed that learns the system parameters and the transmission policy in real time. The effectiveness of the proposed methods are verified through numerical simulations.
Somuyiwa SO, Gyorgy A, Gunduz D, 2018, A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 36, Pages: 1331-1344, ISSN: 0733-8716
Yang Q, Gunduz D, 2018, Coded Caching and Content Delivery With Heterogeneous Distortion Requirements, IEEE TRANSACTIONS ON INFORMATION THEORY, Vol: 64, Pages: 4347-4364, ISSN: 0018-9448
Bharath BN, Nagananda KG, Gunduz D, et al., 2018, Caching With Time-Varying Popularity Profiles: A Learning-Theoretic Perspective, IEEE Transactions on Communications, ISSN: 0090-6778
IEEE Content caching at the small-cell base stations (sBSs) in a heterogeneous wireless network is considered. A cost function is proposed that captures the backhaul link load called the “offloading loss”, which measures the fraction of the requested files that are not available in the sBS caches. As opposed to the previous approaches that consider time-invariant and perfectly known popularity profile, caching with non-stationary and statistically dependent popularity profiles (assumed unknown, and hence, estimated) is studied from a learning-theoretic perspective. A probably approximately correct result is derived, which presents a high probability bound on the offloading loss difference, i.e., the error between the estimated and the optimal offloading loss. The difference is a function of the Rademacher complexity, the β-mixing coefficient, the number of time slots, and a measure of discrepancy between the estimated and true popularity profiles. A cache update algorithm is proposed, and simulation results are presented to show its superiority over periodic updates. The performance analyses for Bernoulli and Poisson request models are also presented.
Ozfatura E, Gunduz D, 2018, Mobility and Popularity-Aware Coded Small-Cell Caching, IEEE COMMUNICATIONS LETTERS, Vol: 22, Pages: 288-291, ISSN: 1089-7798
Mohammadi Amiri M, Gunduz D, 2018, Cache-aided content delivery over erasure broadcast channels, IEEE Transactions on Communications, Vol: 66, Pages: 370-381, ISSN: 0090-6778
A cache-aided broadcast network is studied, in which a server delivers contents to a group of receivers over a packet erasure broadcast channel (BC). The receivers are divided into two sets with regards to their channel qualities: the weak and strong receivers, where all the weak receivers have statistically worse channel qualities than all the strong receivers. The weak receivers, in order to compensate for the high erasure probability they encounter over the channel, are equipped with cache memories of equal size, while the receivers in the strong set have no caches. Data can be pre-delivered to weak receivers’ caches over the off-peak traffic period before the receivers reveal their demands. Allowing arbitrary erasure probabilities for the weak and strong receivers, a joint caching and channel coding scheme, which divides each file into several subfiles, and applies a different caching and delivery scheme for each subfile, is proposed. It is shown that all the receivers, even those without any cache memories, benefit from the presence of caches across the network. An information theoretic trade-off between the cache size and the achievable rate is formulated. It is shown that the proposed scheme improves upon the state-of-the-art in terms of the achievable trade-off.
Giaconi G, Gunduz D, Poor HV, 2018, Smart Meter Privacy With Renewable Energy and an Energy Storage Device, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, Vol: 13, Pages: 129-142, ISSN: 1556-6013
Coon JP, Badiu M-A, Gunduz D, 2018, On the Conditional Entropy of Wireless Networks, 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Publisher: IEEE
Rassouli B, Rosas F, Gunduz D, 2018, Latent Feature Disclosure under Perfect Sample Privacy, 10th IEEE International Workshop on Information Forensics and Security (WIFS), Publisher: IEEE, ISSN: 2157-4766
Ozfatura E, Rarris T, Gunduz D, et al., 2018, Delay-Aware Coded Caching for Mobile Users, 29th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Publisher: IEEE
Faqir OJ, Kerrigan EC, Gunduz D, 2018, Information transmission bounds in mobile communication networks, UKACC 12th International Conference on Control (CONTROL), Publisher: IEEE, Pages: 99-99
Faqir OJ, Kerrigan EC, Gunduz D, 2018, Energy-optimal control in mobile aerial relay-assisted networks, UKACC 12th International Conference on Control (CONTROL), Publisher: IEEE, Pages: 100-100
Rassouli B, Gunduz D, 2018, On Perfect Privacy, IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 2555-2559
Chin J-X, Giaconi G, De Rubira TT, et al., 2018, Considering Time Correlation in the Estimation of Privacy Loss for Consumers with Smart Meters, Power Systems Computation Conference (PSCC), Publisher: IEEE
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