Signal Processing for Cryptography and Security Applications

Knežević, Miroslav; Batina, Lejla; Mulder, Elke De; Fan, Junfeng; Gierlichs, Benedikt; Lee, Yong Ki; Maes, Roel; Verbauwhede, Ingrid

doi:10.1007/978-1-4614-6859-2_7

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…Troncoso-Pastoriza and Perez-Gonzalez have an excellent survey of the problems and solution techniques for privacy in cloud computing [18]. There are many other examples among varying fields; secure signal processing [11], biometrics [19], and [14]. Many specific examples exist that use the Paillier encryption scheme (see [10] for scheme definition).…”

Section: Related Workmentioning

confidence: 99%

Secure SURF with Fully Homomorphic Encryption

Shortell,

Shokoufandeh

2017

Preprint

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Cloud computing is an important part of today's world because offloading computations is a method to reduce costs. In this paper, we investigate computing the Speeded Up Robust Features (SURF) using Fully Homomorphic Encryption (FHE). Performing SURF in FHE enables a method to offload the computations while maintaining security and privacy of the original data. In support of this research, we developed a framework to compute SURF via a rational number based format compatible with FHE. Although floating point (R) to rational numbers (Q) conversion introduces error, our research provides tight bounds on the magnitude of error in terms of parameters of FHE. We empirically verified the proposed method against a set of images at different sizes and showed that our framework accurately computes most of the SURF keypoints in FHE.

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Section: Related Workmentioning

confidence: 99%

Secure SURF with Fully Homomorphic Encryption

Shortell,

Shokoufandeh

2017

Preprint

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“…In (Knezevi, 2013) the authors illustrate how signal processing techniques can be used to design and implement cryptographic and security applications. Implementations on DSP processors of well known hash functions, public-key algorithms are described in detail.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Real Time Implementations of Voice Encryption Algorithms using Blackfin Processors

Duta

Gheorghe

Țăpuș

2016

Proceedings of the 2nd International Conference on Information Systems Security and Privacy

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A large part of the latest research in speech coding and speech encryption algorithms is motivated by the need of obtaining secure military communications, to allow effective operation in a hostile environment. Since the bandwidth of the communication channel is a sensitive problem in military applications, low bitrate speech compression methods and high throughput encryption algorithms are mostly used. Several speech encryption methods are characterized by very strict requirements in power consumption, size, and voltage supply. These requirements are difficult to fulfill, given the complexity and number of functions to be implemented, together with the real time requirement and large dynamic range of the input signals. To meet these constraints, careful optimization should be done at all levels, ranging from algorithmic level, through system and circuit architecture, to layout and design of the cell library. The key points of this optimization are among others, the choice of the algorithms, the modification of the algorithms to reduce computational complexity, the choice of a fixed-point arithmetic unit, the minimization of the number of bits required at every node of the algorithm, and a careful match between algorithms and architecture. This paper describes the performance analysis on Digital Signal Processor (DSP) platform of some of the recently proposed voice encryption algorithms, as well as the performance of stream ciphers such as Grain v1, Trivium and Mickey 2.0 (which are suited for real time voice encryption). The algorithms were ported onto a fixed point DSP, Blackfin 537, and stage by stage optimization was performed to meet the real time requirements. Memory optimization techniques such as data placement and caching were also used to reduce the processing time. The goal was to determine which of the evaluated encryption algorithms is best suited for real time secure communications.

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Secure Feature Extraction in Computational Vision Using Fully Homomorphic Encryption

Shortell

Shokoufandeh

2018

Proceedings of the Future Technologies Conference (FTC) 2018

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Signal Processing for Cryptography and Security Applications

Cited by 5 publications

References 54 publications

Secure SURF with Fully Homomorphic Encryption

Secure SURF with Fully Homomorphic Encryption

Performance Analysis of Real Time Implementations of Voice Encryption Algorithms using Blackfin Processors

Secure Feature Extraction in Computational Vision Using Fully Homomorphic Encryption

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