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As new data records are uploaded by data owners, these data structures are continually updated by Longshot using novel algorithms that leverage bounded information leakage to minimize the use of expensive cryptographic protocols. Furthermore, Long-shot organizes the data structures as a hierarchical tree based on when the update occurred, allowing for update strategies that provide logarithmic error over time. Through this approach, Longshot introduces a tunable three-way trade-off between privacy, accuracy, and efficiency. Our experimental results confirm that our optimizations are not only asymptotic improvements but also observable in practice. In particular, we see a 5x efficiency improvement to update our data structures even when the number of updates is less than 200. Moreover, the data structures significantly improve query runtimes over time, about ~10\n 3<\/jats:sup>\n x faster compared to the baseline after 20 updates.\n <\/jats:p>","DOI":"10.14778\/3594512.3594529","type":"journal-article","created":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T00:28:36Z","timestamp":1687480116000},"page":"2005-2018","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Longshot: Indexing Growing Databases Using MPC and Differential Privacy"],"prefix":"10.14778","volume":"16","author":[{"given":"Yanping","family":"Zhang","sequence":"first","affiliation":[{"name":"Duke University"}]},{"given":"Johes","family":"Bater","sequence":"additional","affiliation":[{"name":"Tufts Univeresity"}]},{"given":"Kartik","family":"Nayak","sequence":"additional","affiliation":[{"name":"Duke University"}]},{"given":"Ashwin","family":"Machanavajjhala","sequence":"additional","affiliation":[{"name":"Duke University"}]}],"member":"320","published-online":{"date-parts":[[2023,6,22]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2022. 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Johes Bater, Gregory Elliott, Craig Eggen, Satyender Goel, Abel Kho, and Jennie Rogers. 2016. SMCQL: secure querying for federated databases. arXiv preprint arXiv:1606.06808 (2016)."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/3291264.3291274"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407854"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20901-7_2"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74143-5_30"},{"key":"e_1_2_1_11_1","volume-title":"The tao of inference in privacy-protected databases. Cryptology ePrint Archive","author":"Bindschaedler Vincent","year":"2017","unstructured":"Vincent Bindschaedler , Paul Grubbs , David Cash , Thomas Ristenpart , and Vitaly Shmatikov . 2017. The tao of inference in privacy-protected databases. Cryptology ePrint Archive ( 2017 ). 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