{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:10:40Z","timestamp":1783383040527,"version":"3.54.6"},"reference-count":0,"publisher":"Universitatsbibliothek der Ruhr-Universitat Bochum","issue":"2","license":[{"start":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T00:00:00Z","timestamp":1710201600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["TCHES"],"abstract":"<jats:p>Secure multi-party computation and homomorphic encryption are two primary security primitives in privacy-preserving machine learning, whose wide adoption is, nevertheless, constrained by the computation and network communication overheads. This paper proposes a hybrid Secret-sharing and Homomorphic encryption Architecture for Privacy-pERsevering machine learning (SHAPER). SHAPER protects sensitive data in encrypted or randomly shared domains instead of relying on a trusted third party. The proposed algorithm-protocol-hardware co-design methodology explores techniques such as plaintext Single Instruction Multiple Data (SIMD) and fine-grained scheduling, to minimize end-to-end latency in various network settings. SHAPER also supports secure domain computing acceleration and the conversion between mainstream privacy-preserving primitives, making it ready for general and distinctive data characteristics. SHAPER is evaluated by FPGA prototyping with a comprehensive hyper-parameter exploration, demonstrating a 94x speed-up over CPU clusters on large-scale logistic regression training tasks.<\/jats:p>","DOI":"10.46586\/tches.v2024.i2.819-843","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T06:40:16Z","timestamp":1710312016000},"page":"819-843","source":"Crossref","is-referenced-by-count":5,"title":["SHAPER: A General Architecture for Privacy-Preserving Primitives in Secure Machine Learning"],"prefix":"10.46586","volume":"2024","author":[{"given":"Ziyuan","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi\u2019ao","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhheng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"25480","published-online":{"date-parts":[[2024,3,12]]},"container-title":["IACR Transactions on Cryptographic Hardware and Embedded Systems"],"original-title":[],"link":[{"URL":"https:\/\/tches.iacr.org\/index.php\/TCHES\/article\/download\/11448\/10953","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/tches.iacr.org\/index.php\/TCHES\/article\/download\/11448\/10953","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T06:40:17Z","timestamp":1710312017000},"score":1,"resource":{"primary":{"URL":"https:\/\/tches.iacr.org\/index.php\/TCHES\/article\/view\/11448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,12]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,3,12]]}},"URL":"https:\/\/doi.org\/10.46586\/tches.v2024.i2.819-843","relation":{},"ISSN":["2569-2925"],"issn-type":[{"value":"2569-2925","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,12]]}}}