Paper 2023/568

Enhancing the Privacy of Machine Learning via faster arithmetic over Torus FHE

Marc Titus Trifan, University of California, Irvine
Alexandru Nicolau, University of California, Irvine
Alexander Veidenbaum, University of California, Irvine

The increased popularity of Machine Learning as a Service (MLaaS) makes the privacy of user data and network weights a critical concern. Using Torus FHE (TFHE) offers a solution for privacy-preserving computation in a cloud environment by allowing computation directly over encrypted data. However, software TFHE implementations of cyphertext-cyphertext multiplication needed when both input data and weights are encrypted are either lacking or are too slow. This paper proposes a new way to improve the performance of such multiplication by applying carry save addition. Its theoretical speedup is proportional to the bit width of the plaintext integer operands. This also speeds up multi-operand summation. A speedup of 15x is obtained for 16-bit multiplication on a 64-core processor, when compared to previous results. Multiplication also becomes more than twice as fast on a GPU if our approach is utilized. This leads to much faster dot product and convolution computations, which combine multiplications and a multi-operand sum. A 45x speedup is achieved for a 16-bit, 32-element dot product and a ~30x speedup for a convolution with a 32x32 filter size.

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Publication info
Published elsewhere. IEEE CSCloud 2023
Fully Homomorphic EncryptionTorus FHEGate BootstrappingCPU ParallelismCarry Save Addition
Contact author(s)
mtrifan @ uci edu
nicolau @ ics uci edu
alexv @ ics uci edu
2023-05-18: last of 2 revisions
2023-04-22: received
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      author = {Marc Titus Trifan and Alexandru Nicolau and Alexander Veidenbaum},
      title = {Enhancing the Privacy of Machine Learning via faster arithmetic over Torus FHE},
      howpublished = {Cryptology ePrint Archive, Paper 2023/568},
      year = {2023},
      note = {\url{}},
      url = {}
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