CPU and GPU accelerated fully homomorphic encryption

dc.contributor.authorTamal, Md Toufique Morshed
dc.contributor.examiningcommitteeRuppa, Thulasiram (Computer Science)en_US
dc.contributor.examiningcommitteeRobert, McLeod (Electrical and Computer Engineering)en_US
dc.contributor.supervisorNoman, Mohammed (Computer Science)en_US
dc.date.accessioned2019-12-10T17:23:30Z
dc.date.available2019-12-10T17:23:30Z
dc.date.issued2019-11en_US
dc.date.submitted2019-12-02T21:08:06Zen
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractFully Homomorphic Encryption (FHE) is one of the most promising technologies for privacy protection as it allows an arbitrary number of function computations over encrypted data. However, the computational cost of these FHE systems limits their widespread applications. In this thesis, our objective is to improve the performance of FHE schemes by designing efficient parallel frameworks. In particular, we choose Torus Fully Homomorphic Encryption (TFHE) as it offers exact results for an infinite number of boolean gate (e.g., AND, XOR) evaluations. We first extend the gate operations to algebraic circuits such as addition, multiplication, and their vector and matrix equivalents. Secondly, we consider the multi-core CPUs to improve the efficiency of both the gate and the arithmetic operations. Finally, we port the TFHE to the Graphics Processing Units (GPU) and device novel optimizations for boolean and arithmetic circuits employing the multitude of cores. We also experimentally analyze both the CPU and GPU parallel frameworks for different numeric representations (16 to 32-bit). Our GPU implementation outperforms the existing techniques, and it achieves a speedup of 20x for any 32-bit boolean operation and 14.5x for multiplications.en_US
dc.description.noteFebruary 2020en_US
dc.identifier.urihttp://hdl.handle.net/1993/34394
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectFully Homomorphic Encryptionen_US
dc.subjectGPU parallelismen_US
dc.subjectSecure computation on GPUen_US
dc.subjectParallel FHE Frameworken_US
dc.titleCPU and GPU accelerated fully homomorphic encryptionen_US
dc.typemaster thesisen_US
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