{"id":3347,"date":"2024-11-24T14:00:27","date_gmt":"2024-11-24T06:00:27","guid":{"rendered":"https:\/\/changjinyuan.com\/?p=3347"},"modified":"2024-12-20T16:28:40","modified_gmt":"2024-12-20T08:28:40","slug":"liu-s-luo-j-zhang-y-wang-h-yu-y-xu-z-2024-efficient-privacy-preserving-gaussian-process-via-secure-multi-party-computation-journal-of-systems-architecture-151-103134","status":"publish","type":"post","link":"https:\/\/changjinyuan.com\/index.php\/en\/publications-en\/publications-all-en\/3347\/","title":{"rendered":"Liu, S., Luo, J., Zhang, Y., Wang, H., Yu, Y., &#038; Xu, Z. (2024). Efficient privacy-preserving Gaussian process via secure multi-party computation. Journal of Systems Architecture, 151, 103134."},"content":{"rendered":"<p>Gaussian processes (GPs), known for their flexibility as non-parametric models, have been widely used inpractice involving sensitive data (e.g., healthcare, finance) from multiple sources. With the challenge of dataisolation and the need for high-performance models, how to jointly develop privacy-preserving GP for multipleparties has emerged as a crucial topic, In this paper, we propose a new privacy-preserving GP algorithm, namelyPP-GP, which employs secret sharing ($$) techniques, Specifically, we introduce a new ss-based exponentiationoperation (PP-Exp) through confusion correction and an SS-based matrix inversion operation (PP-Ml) basedon Cholesky decomposition. However, the advantages of the GP come with a great computational burden andspace cost. To further enhance the efficiency, we propose an efficient split learning framework for privacy.preserving GP, named Split-GP, which demonstrably improves performance on large-scale data. We leave theDrivate data-related and SMPC-hostile computations (i.., random features) on data holders, and delegate therest of SMPC-friendly computations (i.e., low-rank approximation, model construction, and prediction) to semihonest servers. The resulting algorithm significantly reduces computational and communication costs comparedto Pp-GPp, making it well-suited for application to large-scale datasets. We provide a theoretical analysis interms of the correctness and security of the proposed Ss-based operations. Extensive experiments show thatour methods can achieve competitive performance and efficiency under the premise of preserving privacy.<\/p>\r\n\r\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" style=\"width: 100%; height: 600px;\" data=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/11\/Efficient-privacy-preserving-Gaussian-process-via-secure-multi-party-computation.pdf\" type=\"application\/pdf\" width=\"300\" height=\"150\" aria-label=\"\u5d4c\u5165 Efficient privacy-preserving Gaussian process via secure multi-party computation\"><\/object><a id=\"wp-block-file--media-bea85d14-741f-478a-9ce3-b11f96ac753d\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/11\/Efficient-privacy-preserving-Gaussian-process-via-secure-multi-party-computation.pdf\">Efficient privacy-preserving Gaussian process via secure multi-party computation<\/a><a class=\"wp-block-file__button wp-element-button\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/11\/Efficient-privacy-preserving-Gaussian-process-via-secure-multi-party-computation.pdf\" download=\"\" aria-describedby=\"wp-block-file--media-bea85d14-741f-478a-9ce3-b11f96ac753d\">Download<\/a><\/div>\r\n","protected":false},"excerpt":{"rendered":"<p>Gaussian processes (GPs), known for their flexibility as non-parametric models, have been widely used inpractice involving sensitive data (e.g., healthcare, finance) from multiple sources. With the challenge of dataisolation and the need for high-performance models, how to jointly develop privacy-preserving GP for multipleparties has emerged as a crucial topic, In this paper, we propose a new privacy-preserving GP algorithm, namelyPP-GP, which employs secret sharing ($$) techniques, Specifically, we introduce a new ss-based exponentiationoperation (PP-Exp) through confusion correction and an SS-based matrix inversion operation (PP-Ml) basedon Cholesky decomposition. However, the advantages of the GP come with a great computational burden andspace cost. To further enhance the efficiency, we propose an efficient split learning framework for privacy.preserving GP, named Split-GP, which demonstrably improves performance on large-scale data. We leave theDrivate data-related and SMPC-hostile computations (i.., random features) on data holders, and delegate therest of SMPC-friendly computations (i.e., low-rank approximation, model construction, and prediction) to semihonest servers. The resulting algorithm significantly reduces computational and communication costs comparedto Pp-GPp, making it well-suited for application to large-scale datasets. We provide a theoretical analysis interms of the correctness and security of the proposed Ss-based operations. Extensive experiments show thatour methods can achieve competitive performance and efficiency under the premise of preserving privacy.<\/p>\n","protected":false},"author":1,"featured_media":3494,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21],"tags":[],"class_list":["post-3347","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications-all-en"],"acf":[],"lang":"en","translations":{"en":3347,"cn":3345},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/3347","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/comments?post=3347"}],"version-history":[{"count":4,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/3347\/revisions"}],"predecessor-version":[{"id":3498,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/3347\/revisions\/3498"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media\/3494"}],"wp:attachment":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media?parent=3347"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/categories?post=3347"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/tags?post=3347"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}