Invited: Accelerating Fully Homomorphic Encryption with Processing in Memory

Fully homomorphic encryption (FHE) provides a promising solution for future computing needs by allowing privacy-preserving computation. However, its practical use has been limited by the huge latency overhead it incurs while computing. This is primarily due to the huge size of encrypted data and int...

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Vydáno v:2021 58th ACM/IEEE Design Automation Conference (DAC) s. 1335 - 1338
Hlavní autoři: Gupta, Saransh, Rosing, Tajana Simunic
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 05.12.2021
Témata:
Arithmetic
Cryptography
Design automation
fully homomorphic encryption
Logic gates
Parallel processing
processing in-memory
secure computation
secure learning
Throughput
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Abstract Fully homomorphic encryption (FHE) provides a promising solution for future computing needs by allowing privacy-preserving computation. However, its practical use has been limited by the huge latency overhead it incurs while computing. This is primarily due to the huge size of encrypted data and intermediate processing required to compute on it. In this paper, we present insights into the benefits of accelerating FHE with processing in-memory (PIM). PIM is an excellent match for the FHE since it provides extensive parallelism, in-situ operations, and bit-level granularity. We present FHE-PIM, which implements basic polynomial primitives with PIM and uses them to accelerate key FHE operations in memory. This can significantly make the time-consuming procedure of FHE bootstrapping faster in memory. We compare the speedup of FHE-PIM for various FHE operations with their CPU implementations. FHE-PIM can achieve an estimated average throughput improvement of 88,397 \times as compared to CPU for FHE arithmetic operations.
AbstractList Fully homomorphic encryption (FHE) provides a promising solution for future computing needs by allowing privacy-preserving computation. However, its practical use has been limited by the huge latency overhead it incurs while computing. This is primarily due to the huge size of encrypted data and intermediate processing required to compute on it. In this paper, we present insights into the benefits of accelerating FHE with processing in-memory (PIM). PIM is an excellent match for the FHE since it provides extensive parallelism, in-situ operations, and bit-level granularity. We present FHE-PIM, which implements basic polynomial primitives with PIM and uses them to accelerate key FHE operations in memory. This can significantly make the time-consuming procedure of FHE bootstrapping faster in memory. We compare the speedup of FHE-PIM for various FHE operations with their CPU implementations. FHE-PIM can achieve an estimated average throughput improvement of 88,397 \times as compared to CPU for FHE arithmetic operations.
Author Rosing, Tajana Simunic
Gupta, Saransh
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  givenname: Tajana Simunic
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  fullname: Rosing, Tajana Simunic
  email: tajana@ucsd.edu
  organization: University of California,Computer Science and Engineering,San Diego La Jolla,USA
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Snippet Fully homomorphic encryption (FHE) provides a promising solution for future computing needs by allowing privacy-preserving computation. However, its practical...
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StartPage 1335
SubjectTerms Arithmetic
Cryptography
Design automation
fully homomorphic encryption
Logic gates
Parallel processing
processing in-memory
secure computation
secure learning
Throughput
Title Invited: Accelerating Fully Homomorphic Encryption with Processing in Memory
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