{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T16:40:19Z","timestamp":1756485619208,"version":"3.44.0"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:p>In aggregation queries, predicate parameters often reveal user intent. Protecting these parameters is critical for user privacy, regardless of whether the database is public or private. While most existing works focus on private data settings, we address a public data setting where the server has access to the database. Current solutions for this setting either require additional setups (e.g., non-colluding servers, hardware enclaves) or are inefficient for practical workloads. Furthermore, they often do not support range predicates or boolean combinations commonly seen in real-world use cases.<\/jats:p>\n          <jats:p>To address these limitations, we built HADES, a fully homomorphic encryption (FHE) based private aggregation system for public data that supports point, range predicates, and boolean combinations. Our one-round HADES protocol efficiently generates predicate indicators by leveraging the plaintext form of public data records. It introduces a novel elementwise-mapping operation and an optimized reduction algorithm, achieving latency efficiency within a limited noise budget. Our highly scalable, multi-threaded implementation improves performance over previous one-round FHE solutions by 204x to 6574x on end-to-end TPC-H queries, reducing aggregation time on 1M records from 15 hours to 38 seconds.<\/jats:p>","DOI":"10.14778\/3734839.3734852","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T16:01:06Z","timestamp":1756483266000},"page":"2159-2171","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["HADES: Range-Filtered Private Aggregation on Public Data"],"prefix":"10.14778","volume":"18","author":[{"given":"Xiaoyuan","family":"Liu","sequence":"first","affiliation":[{"name":"UC Berkeley"}]},{"given":"Ni","family":"Trieu","sequence":"additional","affiliation":[{"name":"Arizona State University"}]},{"given":"Trinabh","family":"Gupta","sequence":"additional","affiliation":[{"name":"University of California Santa Barbara"}]},{"given":"Ishtiyaque","family":"Ahmad","sequence":"additional","affiliation":[{"name":"University of California Santa Cruz"}]},{"given":"Dawn","family":"Song","sequence":"additional","affiliation":[{"name":"UC Berkeley"}]}],"member":"320","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/3574245.3574251"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477132.3483586"},{"key":"e_1_2_1_3_1","volume-title":"International Conference on Selected Areas in Cryptography. 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Bulletin of the EATCS 82, 72\u2013107 (2004), 113.","journal-title":"Bulletin of the EATCS"},{"key":"e_1_2_1_12_1","volume-title":"Private Aggregate Queries to Untrusted Databases. arXiv preprint arXiv:2403.13296","author":"Hafiz Syed Mahbub","year":"2024","unstructured":"Syed Mahbub Hafiz, Chitrabhanu Gupta, Warren Wnuck, Brijesh Vora, and Chen-Nee Chuah. 2024. Private Aggregate Queries to Untrusted Databases. arXiv preprint arXiv:2403.13296 (2024)."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10207-023-00743-6"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2016.2568182"},{"key":"e_1_2_1_15_1","volume-title":"Simple encrypted arithmetic library 2.3. 1. Microsoft Research https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2017\/11\/sealmanual-2-3-1.pdf","author":"Laine Kim","year":"2017","unstructured":"Kim Laine. 2017. Simple encrypted arithmetic library 2.3. 1. 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