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Database Syst."],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>Hardware-enabled enclaves have been applied to efficiently enforce data security and privacy protection in cloud database services. Such enclaved systems, however, are reported to suffer from I\/O-size (also referred to as communication-volume)-based side-channel attacks. Albeit differentially private padding has been exploited to defend against these attacks as a principle method, it introduces a challenging bi-objective parametric query optimization (BPQO) problem and current solutions are still not satisfactory. Concretely, the goal in BPQO is to find a Pareto-optimal plan that makes a tradeoff between query performance and privacy loss; existing solutions are subjected to poor computational efficiency and high cloud resource waste. In this article, we propose a two-phase optimization algorithm called TPOA to solve the BPQO problem. TPOA incorporates two novel ideas:<jats:italic>divide-and-conquer<\/jats:italic>to separately handle parameters according to their types in optimization for dimensionality reduction;<jats:italic>on-demand-optimization<\/jats:italic>to progressively build a set of necessary Pareto-optimal plans instead of seeking a complete set for saving resources. Besides, we introduce an acceleration mechanism in TPOA to improve its efficiency, which prunes the non-optimal candidate plans in advance. We theoretically prove the correctness of TPOA, numerically analyze its complexity, and formally give an end-to-end privacy analysis. Through a comprehensive evaluation on its efficiency by running baseline algorithms over synthetic and test-bed benchmarks, we can conclude that TPOA outperforms all benchmarked methods with an overall efficiency improvement of roughly two orders of magnitude; moreover, the acceleration mechanism speeds up TPOA by 10-200\u00d7.<\/jats:p>","DOI":"10.1145\/3597021","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T12:20:37Z","timestamp":1683807637000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient Bi-objective SQL Optimization for Enclaved Cloud Databases with Differentially Private Padding"],"prefix":"10.1145","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3945-8468","authenticated-orcid":false,"given":"Yaxing","family":"Chen","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8436-4754","authenticated-orcid":false,"given":"Qinghua","family":"Zheng","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9697-2108","authenticated-orcid":false,"given":"Zheng","family":"Yan","sequence":"additional","affiliation":[{"name":"Xidian University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"13635","article-title":"An algorithmic framework for differentially private data analysis on trusted processors","author":"Allen Joshua","year":"2019","unstructured":"Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, and Sergey Yekhanin. 2019. 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