{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:45:02Z","timestamp":1774622702939,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":12,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T00:00:00Z","timestamp":1641600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,1,8]]},"DOI":"10.1145\/3493700.3493760","type":"proceedings-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T23:54:21Z","timestamp":1641599661000},"page":"318-319","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["PPDL - Privacy Preserving Deep Learning Using Homomorphic Encryption"],"prefix":"10.1145","author":[{"given":"Nayna","family":"Jain","sequence":"first","affiliation":[{"name":"International Institute of Information Technology Bangalore, IN and IBM Systems, USA"}]},{"given":"Karthik","family":"Nandakumar","sequence":"additional","affiliation":[{"name":"Mohamed Bin Zayed University of Artificial Intelligence, UAE"}]},{"given":"Nalini","family":"Ratha","sequence":"additional","affiliation":[{"name":"University at Buffalo, US"}]},{"given":"Sharath","family":"Pankanti","sequence":"additional","affiliation":[{"name":"Microsoft, US"}]},{"given":"Uttam","family":"Kumar","sequence":"additional","affiliation":[{"name":"International Institute of Information Technology Bangalore, IN"}]}],"member":"320","published-online":{"date-parts":[[2022,1,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Fabian Boemer Anamaria Costache Rosario Cammarota and Casimir Wierzynski. 2019. nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data. arxiv:1908.04172\u00a0[cs.CR]","DOI":"10.1145\/3338469.3358944"},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Machine Learning. 812\u2013821","author":"Brutzkus Alon","year":"2019","unstructured":"Alon Brutzkus, Ran Gilad-Bachrach, and Oren Elisha. 2019. Low latency privacy preserving inference. In International Conference on Machine Learning. 812\u2013821."},{"key":"e_1_3_2_1_3_1","unstructured":"Edward Chou Josh Beal Daniel Levy Serena Yeung Albert Haque and Li Fei-Fei. 2018. Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference. CoRR abs\/1811.09953(2018). arxiv:1811.09953http:\/\/arxiv.org\/abs\/1811.09953"},{"key":"e_1_3_2_1_4_1","volume-title":"International Conference on Machine Learning. 201\u2013210","author":"Dowlin Nathan","year":"2016","unstructured":"Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016. Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In International Conference on Machine Learning. 201\u2013210."},{"key":"e_1_3_2_1_5_1","unstructured":"Zahra Ghodsi Akshaj Veldanda Brandon Reagen and Siddharth Garg. 2021. CryptoNAS: Private Inference on a ReLU Budget. arxiv:2006.08733\u00a0[cs.LG]"},{"key":"e_1_3_2_1_6_1","volume-title":"Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI)","author":"Gutman A.","year":"2016","unstructured":"David\u00a0A. Gutman, Noel C.\u00a0F. Codella, M.\u00a0Emre Celebi, Brian Helba, Michael\u00a0A. Marchetti, Nabin\u00a0K. Mishra, and Allan Halpern. 2016. Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC). CoRR abs\/1605.01397(2016). arXiv:1605.01397http:\/\/arxiv.org\/abs\/1605.01397"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Takumi Ishiyama Takuya Suzuki and Hayato Yamana. 2020. Highly Accurate CNN Inference Using Approximate Activation Functions over Homomorphic Encryption. arxiv:2009.03727\u00a0[cs.LG]","DOI":"10.1109\/BigData50022.2020.9378372"},{"key":"e_1_3_2_1_8_1","unstructured":"Nandan\u00a0Kumar Jha Zahra Ghodsi Siddharth Garg and Brandon Reagen. 2021. DeepReDuce: ReLU Reduction for Fast Private Inference. arxiv:2103.01396\u00a0[cs.LG]"},{"key":"e_1_3_2_1_9_1","volume-title":"27th USENIX Security Symposium (USENIX Security 18)","author":"Juvekar Chiraag","year":"2018","unstructured":"Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan. 2018. GAZELLE: A low latency framework for secure neural network inference. In 27th USENIX Security Symposium (USENIX Security 18). 1651\u20131669."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/tc.2021.3076123"},{"key":"e_1_3_2_1_11_1","volume-title":"SHE: A Fast and Accurate Deep Neural Network for Encrypted Data. In Advances in Neural Information Processing Systems. 10035\u201310043.","author":"Lou Qian","year":"2019","unstructured":"Qian Lou and Lei Jiang. 2019. SHE: A Fast and Accurate Deep Neural Network for Encrypted Data. In Advances in Neural Information Processing Systems. 10035\u201310043."},{"key":"e_1_3_2_1_12_1","volume-title":"HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture. arxiv:2106.00038\u00a0[cs.CR]","author":"Lou Qian","year":"2021","unstructured":"Qian Lou and Lei Jiang. 2021. HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture. arxiv:2106.00038\u00a0[cs.CR]"}],"event":{"name":"CODS-COMAD 2022: 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)","location":"Bangalore India","acronym":"CODS-COMAD 2022","sponsor":["SIGGRAPH ACM Special Interest Group on Computer Graphics and Interactive Techniques"]},"container-title":["Proceedings of the 5th Joint International Conference on Data Science &amp; Management of Data (9th ACM IKDD CODS and 27th COMAD)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3493700.3493760","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3493700.3493760","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:44Z","timestamp":1750188644000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3493700.3493760"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,8]]},"references-count":12,"alternative-id":["10.1145\/3493700.3493760","10.1145\/3493700"],"URL":"https:\/\/doi.org\/10.1145\/3493700.3493760","relation":{},"subject":[],"published":{"date-parts":[[2022,1,8]]},"assertion":[{"value":"2022-01-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}