Information Theoretically Secure Communication Under Channel Uncertainty

Author : Yuksel Ozan Basciftci
Publisher :
ISBN 13 :
Total Pages : 143 pages
Book Rating : 4.:/5 (984 download)

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Download or read book Private and Secure Data Communication written by Yuksel Ozan Basciftci and published by . This book was released on 2016 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wireless networks flourishing worldwide enable online services, such as social networks and search engines to serve huge number of users and to collect large amount of data about their users. Sharing of this data has been key driver of innovation and improvement in the quality of these services, but also raised major security and privacy concerns. This thesis aims to address privacy concerns in data sharing as well as security concerns in wireless data communication using information theoretic framework. In the first part of the thesis, we build security establishing algorithms that bring unbreakable security to wireless data communication. The broadcast nature of wireless medium makes data communication susceptible to various security attacks. For instance, an adversary can eavesdrop on confidential data traffic without actually tapping a wire or optical fiber, or block the data traffic by transmitting meaningless but powerful radio signals. First, we study point-to-point communication in the presence of a hybrid adversary. The hybrid half-duplex adversary can choose to either eavesdrop or jam the transmitter-receiver channel in arbitrary manner. The goal of the transmitter is to communicate a message reliably to the receiver while keeping it asymptotically secret from the hybrid adversary. We show that, without any feedback from the receiver, the channel capacity is zero if the transmitter-to-adversary channel stochastically dominates the effective transmitter-to-receiver channel. However, the channel capacity is non-zero even when the receiver is allowed to feedback only one bit periodically, that describes the transmitter-to-receiver channel quality. Our novel achievable strategy improves the rates proposed in the literature for the non-hybrid adversarial model. Then, we study the security of a single-cell downlink massive multiple input multiple output (MIMO) communication in the presence of an adversary capable of jamming and eavesdropping simultaneously. After showing massive MIMO communication is naturally resilient to no training-phase jamming attack in which the adversary jams only the data communication and eavesdrops both the data communication and the training, we evaluate the number of antennas that base station (BS) requires in order to establish information theoretic security without even a need for extra security encoding. Next, we show that things are completely different once the adversary starts jamming the training phase. Specifically, we consider an attack, called training-phase jamming in which the adversary jams and eavesdrops both the training and the data communication. We show that under such an attack, the maximum secure degrees of freedom (DoF) is equal to zero. To counter this attack, we develop a defense strategy in which we use a secret key to encrypt the pilot sequence assignments to hide them from the adversary, rather than encrypt the data. We show that, if the cardinality of the set of pilot signals are scaled appropriately, hiding the pilot signal assignments from the adversary enables the users to achieve secure DoF, identical to the maximum achievable DoF under no attack. The last part of the thesis is devoted to developing a mathematical framework for privacy-preserving data release mechanisms. The objective of privacy-preserving data release is to provide useful data with minimal distortion while simultaneously minimizing the sensitive data revealed. Dependencies between the sensitive and useful data results in a privacy-utility tradeoff that has strong connections to generalized rate-distortion problems. In this work, we study how the optimal privacy-utility tradeoff region is affected by constraints on the data that is directly available as input to the release mechanism. Such constraints are potentially motivated by applications where either the sensitive or useful data is not directly observable. For example, the useful data may be an unknown property that must be inferred from only the sensitive data. In particular, we consider the availability of only sensitive data, only useful data, and both (full data). We show that a general hierarchy holds, that is, the tradeoff region given only the sensitive data is no larger than the region given only the useful data, which in turn is clearly no larger than the region given both sensitive and useful data. In addition, we determine conditions under which the tradeoff region given only the useful data coincides with that given full data.