Abstract
Data sharing faces significant constraints with business competition intensifying and privacy regulations enacted. Secure collaborative analysis, particularly in group-by-aggregation queries, has gained significant attention as a way to perform data analysis without directly sharing data among multiple data owners. Previous work has explored outsourced databases, computing, and interactive approaches, but they all have limitations. Outsourced approaches rely on multiple third-party servers, limiting practicality. Interactive approaches can avoid this limitation, but existing solutions have lower efficiency for group-by-aggregation queries.In this paper, we propose Segam, a secure collaborative analytics system designed to execute group-by-aggregation queries across multiple private databases. We leverage Shamir’s secret sharing scheme to design secure operators, ensuring that sensitive data remains confidential. To strike a balance between efficiency and security, we adopt a query decomposition strategy, transforming the execution into a multi-round process that combines both plaintext and secure operators. Our comprehensive experiments validate Segam’s efficiency and usability, showing impressive performance improvements of at least 101.0x in time consumption and 8,186.6x in communication costs compared to previous open-source work.
This work is supported by Natural Science Foundation of Jiangsu Province (Grant Nos. BK20211307), and by project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
IRDiRC - International Rare Diseases Research Consortium. https://irdirc.org/ (2023), [Online; accessed 7-October-2023]
Aggarwal, G., et al.: Secure computation of the k th-ranked element. In: Eurocrypt. pp. 40–55 (2004)
Aggarwal, G., et al.: Secure computation of the median (and other elements of specified ranks). J. Cryptol. 23(3), 373–401 (2010)
Bater, J., et al.: Smcql: Secure query processing for private data networks. PVLDB 10(6), 673–684 (2017)
Bater, J., et al.: Shrinkwrap: efficient sql query processing in differentially private data federations. PVLDB 12(3), 307–320 (2018)
Bater, J., et al.: Saqe: practical privacy-preserving approximate query processing for data federations. PVLDB 13(12), 2691–2705 (2020)
Hackenjos, T., et al.: Sagma: secure aggregation grouped by multiple attributes. In: SIGMOD. pp. 587–601 (2020)
Han, F., et al.: Scape: Scalable collaborative analytics system on private database with malicious security. In: ICDE. pp. 1740–1753 (2022)
Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proceedings of the IRE 40(9), 1098–1101 (1952)
Jurczyk, P., Xiong, L.: Information sharing across private databases: Secure union revisited. In: PASSAT/SocialCom. pp. 996–1003 (2011)
Li, Y., et al.: Prism: Private verifiable set computation over multi-owner outsourced databases. In: SIGMOD. pp. 1116–1128 (2021)
Liagouris, J., et al.: Secrecy: Secure collaborative analytics in untrusted clouds. In: NSDI. pp. 1031–1056 (2023)
Liu, C., et al.: Oblivm: A programming framework for secure computation. In: IEEE Symposium on Security and Privacy. pp. 359–376 (2015)
O’Neil, P., et al.: The star schema benchmark and augmented fact table indexing. In: TPCTC. vol. 5895, pp. 237–252 (2009)
Poddar, R., et al.: Senate: A maliciously-secure mpc platform for collaborative analytics. In: USENIX Security Symposium. pp. 2129–2146 (2021)
Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression (1998)
Shamir, A.: How to share a secret. Communications of the ACM 22(11), 612–613 (1979)
Tong, Y., et al.: Hu-fu: Efficient and secure spatial queries over data federation. PVLDB 15(6), 1159–1172 (2022)
Voigt, P., Von dem Bussche, A.: The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing 10(3152676), 10–5555 (2017)
Volgushev, N., et al.: Conclave: secure multi-party computation on big data. In: EuroSys. pp. 1–18 (2019)
Yao, A.C.: Protocols for secure computations. In: FOCS. pp. 160–164 (1982)
Zahur, S., Evans, D.: Obliv-c: A language for extensible data-oblivious computation. Cryptology ePrint Archive p. 1153 (2015)
Zhao, C., et al.: Secure multi-party computation: theory, practice and applications. Information Sciences 476, 357–372 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cao, Z., Ma, Q., Chen, W., Zhao, L., Liu, A. (2024). Segam: Secure and Efficient Group-by-Aggregation Queries across Multiple Private Database*. In: Onizuka, M., et al. Database Systems for Advanced Applications. DASFAA 2024. Lecture Notes in Computer Science, vol 14853. Springer, Singapore. https://doi.org/10.1007/978-981-97-5562-2_8
Download citation
DOI: https://doi.org/10.1007/978-981-97-5562-2_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5561-5
Online ISBN: 978-981-97-5562-2
eBook Packages: Computer ScienceComputer Science (R0)