ObliuSky: Oblivious User-Defined Skyline Query Processing in the Cloud (2310.07148v1)
Abstract: The proliferation of cloud computing has greatly spurred the popularity of outsourced database storage and management, in which the cloud holding outsourced databases can process database queries on demand. Among others, skyline queries play an important role in the database field due to its prominent usefulness in multi-criteria decision support systems. To accommodate the tailored needs of users, user-defined skyline query has recently emerged as an intriguing advanced type of skyline query, which allows users to define custom preferences in their skyline queries (including the target attributes, preferred dominance relations, and range constraints on the target attributes). However, user-defined skyline query services, if deployed in the cloud, may raise critical privacy concerns as the outsourced databases and skyline queries may contain proprietary/privacy-sensitive information, and the cloud might even suffer from data breaches. In light of the above, this paper presents ObliuSky, a new system framework enabling oblivious user-defined skyline query processing in the cloud. ObliuSky departs from the state-of-the-art prior work by not only providing confidentiality protection for the content of the outsourced database, the user-defined skyline query, and the query results, but also making the cloud oblivious to the data patterns (e.g., user-defined dominance relations among database points and search access patterns) which may indirectly cause data leakages. We formally analyze the security guarantees and conduct extensive performance evaluations. The results show that while achieving much stronger security guarantees than the state-of-the-art prior work, ObliuSky is superior in database and query encryption efficiency, with practically affordable query latency.
- W. Balke, U. Güntzer, and J. X. Zheng, “Efficient distributed skylining for web information systems,” in Proc. of EDBT, 2004.
- Z. Huang, C. S. Jensen, H. Lu, and B. C. Ooi, “Skyline queries against mobile lightweight devices in MANETs,” in Proc. of IEEE ICDE, 2006.
- K. Deng, X. Zhou, and H. T. Shen, “Multi-source skyline query processing in road networks,” in Proc. of IEEE ICDE, 2007.
- Z. Qin, J. Weng, Y. Cui, and K. Ren, “Privacy-preserving image processing in the cloud,” IEEE Cloud Computing, vol. 5, no. 2, pp. 48–57, 2018.
- P. Jiang, Q. Wang, M. Huang, C. Wang, Q. Li, C. Shen, and K. Ren, “Building in-the-cloud network functions: Security and privacy challenges,” Proceedings of the IEEE, vol. 109, no. 12, pp. 1888–1919, 2021.
- J. Liu, J. Yang, L. Xiong, and J. Pei, “Secure and efficient skyline queries on encrypted data,” IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 7, pp. 1397–1411, 2019.
- S. Zhang, S. Ray, R. Lu, Y. Zheng, Y. Guan, and J. Shao, “Towards efficient and privacy-preserving user-defined skyline query over single cloud,” IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 2, pp. 1319–1334, 2023.
- X. Ding, Z. Wang, P. Zhou, K.-K. R. Choo, and H. Jin, “Efficient and privacy-preserving multi-party skyline queries over encrypted data,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 4589–4604, 2021.
- J. Liu, J. Yang, L. Xiong, and J. Pei, “Secure skyline queries on cloud platform,” in Proc. of IEEE ICDE, 2017.
- Y. Zheng, W. Wang, S. Wang, X. Jia, H. Huang, and C. Wang, “Secskyline: Fast privacy-preserving skyline queries over encrypted cloud databases,” IEEE Transactions on Knowledge and Data Engineering, 2022, doi: 10.1109/TKDE.2022.3220595.
- J. Wang, M. Du, and S. S. M. Chow, “Stargazing in the dark: Secure skyline queries with SGX,” in Proc. of DASFAA, 2020.
- X. Liu, K.-K. R. Choo, R. H. Deng, Y. Yang, and Y. Zhang, “Pusc: Privacy-preserving user-centric skyline computation over multiple encrypted domains,” in Proc. of TrustCom/BigDataSE, 2018.
- D. Demmler, T. Schneider, and M. Zohner, “ABY - A framework for efficient mixed-protocol secure two-party computation,” in Proc. of NDSS, 2015.
- S. Börzsönyi, D. Kossmann, and K. Stocker, “The skyline operator,” in Proc. of IEEE ICDE, 2001.
- J. Liu, H. Zhang, L. Xiong, H. Li, and J. Luo, “Finding probabilistic k-skyline sets on uncertain data,” in Proc. of ACM CIKM, 2015.
- J. Pei, B. Jiang, X. Lin, and Y. Yuan, “Probabilistic skylines on uncertain data,” in Proc. of VLDB, 2007.
- E. Dellis, A. Vlachou, I. Vladimirskiy, B. Seeger, and Y. Theodoridis, “Constrained subspace skyline computation,” in Proc. of ACM CIKM, 2006.
- J. Pei, W. Jin, M. Ester, and Y. Tao, “Catching the best views of skyline: A semantic approach based on decisive subspaces,” in Proc. of VLDB, 2005.
- J. Liu, L. Xiong, J. Pei, J. Luo, and H. Zhang, “Finding pareto optimal groups: Group-based skyline,” Proceedings of the VLDB Endowment, vol. 8, no. 13, pp. 2086–2097, 2015.
- W. Yu, Z. Qin, J. Liu, L. Xiong, X. Chen, and H. Zhang, “Fast algorithms for pareto optimal group-based skyline,” in Proc. of ACM CIKM, 2017.
- S. Bothe, A. Cuzzocrea, P. Karras, and A. Vlachou, “Skyline query processing over encrypted data: An attribute-order-preserving-free approach,” in Proc. of International Workshop on Privacy and Security of Big Data, 2014.
- M. Hähnel, W. Cui, and M. Peinado, “High-resolution side channels for untrusted operating systems,” in Proc. of USENIX ATC, 2017.
- J. Van Bulck, N. Weichbrodt, R. Kapitza, F. Piessens, and R. Strackx, “Telling your secrets without page faults: Stealthy page table-based attacks on enclaved execution,” in Proc. of USENIX Security, 2017.
- S. Lee, M.-W. Shih, P. Gera, T. Kim, H. Kim, and M. Peinado, “Inferring fine-grained control flow inside sgx enclaves with branch shadowing,” in Proc. of USENIX Security, 2017.
- D. Lee, D. Jung, I. T. Fang, C.-C. Tsai, and R. A. Popa, “An off-chip attack on hardware enclaves via the memory bus,” in Proc. of USENIX Security, 2020.
- Z. Wang, X. Ding, H. Jin, and P. Zhou, “Efficient secure and verifiable location-based skyline queries over encrypted data,” Proceedings of the VLDB Endowment, vol. 15, no. 9, pp. 1822–1834, 2022.
- M. S. Riazi, C. Weinert, O. Tkachenko, E. M. Songhori, T. Schneider, and F. Koushanfar, “Chameleon: A hybrid secure computation framework for machine learning applications,” in Proc. of ACM AsiaCCS, 2018.
- X. Meng, H. Zhu, and G. Kollios, “Top-k query processing on encrypted databases with strong security guarantees,” in Proc. of IEEE ICDE, 2018.
- W. Chen and R. A. Popa, “Metal: A metadata-hiding file-sharing system,” in Proc. of NDSS, 2020.
- M. Du, S. Wu, Q. Wang, D. Chen, P. Jiang, and A. Mohaisen, “Graphshield: Dynamic large graphs for secure queries with forward privacy,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 7, pp. 3295–3308, 2020.
- N. Cui, X. Yang, B. Wang, J. Li, and G. Wang, “Svknn: Efficient secure and verifiable k-nearest neighbor query on the cloud platform,” in Proc. of IEEE ICDE, 2020.
- Mozilla Security Blog, “Next steps in privacy-preserving Telemetry with Prio.” online at https://blog.mozilla.org/security/2019/06/06/next-steps-in-privacy-preserving-telemetry-with-prio/, 2022.
- Apple and Google, “Exposure Notification Privacy-preserving Analytics (ENPA) White Paper.” online at https://covid19-static.cdn-apple.com/applications/covid19/current/static/contact-tracing/pdf/ENPA_White_Paper.pdf, 2021.
- S. Eskandarian and D. Boneh, “Clarion: Anonymous communication from multiparty shuffling protocols,” in Proc. of NDSS, 2022.
- X. Liu, Y. Zheng, X. Yuan, and X. Yi, “Medisc: Towards secure and lightweight deep learning as a medical diagnostic service,” in Proc. of ESORICS, 2021.
- Y. Lindell, “How to simulate it - a tutorial on the simulation proof technique,” in Tutorials on the Foundations of Cryptography, 2017, pp. 277–346.
- R. Canetti, “Security and composition of multiparty cryptographic protocols,” Journal of Cryptology, vol. 13, no. 1, pp. 143–202, 2000.
- J. Katz and Y. Lindell, “Handling expected polynomial-time strategies in simulation-based security proofs,” in Proc. of TCC, 2005.
- M. Curran, X. Liang, H. Gupta, O. Pandey, and S. R. Das, “Procsa: Protecting privacy in crowdsourced spectrum allocation,” in Proc. of ESORICS, 2019.
- Yifeng Zheng (29 papers)
- Weibo Wang (4 papers)
- Songlei Wang (8 papers)
- Zhongyun Hua (17 papers)
- Yansong Gao (73 papers)