Paper 2015/462

Accelerating SWHE based PIRs using GPUs

Wei Dai, Yarkın Doröz, and Berk Sunar


In this work we focus on tailoring and optimizing the computational Private Information Retrieval (cPIR) scheme proposed in WAHC 2014 for efficient execution on graphics processing units (GPUs). Exploiting the mass parallelism in GPUs is a commonly used approach in speeding up cPIRs. Our goal is to eliminate the efficiency bottleneck of the Doröz et al construction which would allow us to take advantage of its excellent bandwidth performance. To this end, we develop custom code to support polynomial ring operations and extend them to realize the evaluation functions in an optimized manner on high end GPUs. Specifically, we develop optimized CUDA code to support large degree/large coefficient polynomial arithmetic operations such as modular multiplication/reduction, and modulus switching. Moreover, we choose same prime numbers for both the CRT domain representation of the polynomials and for the modulus switching implementation of the somewhat homomorphic encryption scheme. This allows us to combine two arithmetic domains, which reduces the number of domain conversions and permits us to perform faster arithmetic. Our implementation achieves 14-34 times speedup for index comparison and 4-18 times speedup for data aggregation compared to a pure CPU software implementation. tion compared to a pure CPU software implementation.

Available format(s)
Publication info
Published elsewhere. 3rd Workshop on Encrypted Computing and Applied Homomorphic Cryptography - WAHC'15
Private information retrievalhomomorphic encryptionNTRU
Contact author(s)
wdai @ wpi edu
2015-05-15: received
Short URL
Creative Commons Attribution


      author = {Wei Dai and Yarkın Doröz and Berk Sunar},
      title = {Accelerating SWHE based PIRs using GPUs},
      howpublished = {Cryptology ePrint Archive, Paper 2015/462},
      year = {2015},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.