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Embed. Comput. Syst."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>\n            The increasing amount of data and the growing complexity of problems have resulted in an ever-growing reliance on cloud computing. However, many applications, most notably in healthcare, finance, or defense, demand security and privacy, which today\u2019s solutions cannot fully address. Fully homomorphic encryption (FHE) elevates the bar of today\u2019s solutions by adding confidentiality of data during processing. It allows computation on fully encrypted data without the need for decryption, thus fully preserving privacy. To enable processing encrypted data at usable levels of classic security, e.g., 128-bit, the encryption procedure introduces noticeable data size expansion\u2014the ciphertext is much bigger than the native aggregate of native data types. In this article, we present MemFHE, which is the first accelerator of both client and server for the latest Ring-GSW (Gentry et\u00a0al. [\n            <jats:xref ref-type=\"bibr\">17<\/jats:xref>\n            ])-based homomorphic encryption schemes using Processing in Memory (PIM). PIM alleviates the data movement issues with large FHE encrypted data while providing in situ execution and extensive parallelism needed for FHE\u2019s polynomial operations. While the client-PIM can homomorphically encrypt and decrypt data, the server-PIM can process homomorphically encrypted data without decryption. MemFHE\u2019s server-PIM is pipelined and is designed to provide flexible bootstrapping, allowing two encryption techniques and various FHE security levels based on the application requirements. We evaluate MemFHE for various security levels and compare it with state-of-the-art CPU implementations for Ring-GSW-based FHE. MemFHE is up to 20\n            <jats:italic>k<\/jats:italic>\n            \u00d7 (265\u00d7) faster than CPU (GPU) for FHE arithmetic operations and provides on average 2,007\u00d7 higher throughput than [\n            <jats:xref ref-type=\"bibr\">36<\/jats:xref>\n            ] while implementing neural networks with FHE.\n          <\/jats:p>\n          <jats:p\/>\n          <jats:p\/>","DOI":"10.1145\/3569955","type":"journal-article","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T12:02:39Z","timestamp":1667304159000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["MemFHE: End-to-end Computing with Fully Homomorphic Encryption in Memory"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5814-3934","authenticated-orcid":false,"given":"Saransh","family":"Gupta","sequence":"first","affiliation":[{"name":"University of California, San Diego, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2965-8987","authenticated-orcid":false,"given":"Rosario","family":"Cammarota","sequence":"additional","affiliation":[{"name":"Intel Labs, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6954-997X","authenticated-orcid":false,"given":"Tajana","family":"\u0160imuni\u0107","sequence":"additional","affiliation":[{"name":"University of California, San Diego, California, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"297","volume-title":"Annual Cryptology Conference","author":"Alperin-Sheriff Jacob","year":"2014","unstructured":"Jacob Alperin-Sheriff and Chris Peikert. 2014. 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