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SPECIAL: Synopsis Assisted Secure Collaborative Analytics (2404.18388v1)

Published 29 Apr 2024 in cs.CR and cs.DB

Abstract: Secure collaborative analytics (SCA) enable the processing of analytical SQL queries across multiple owners' data, even when direct data sharing is not feasible. Although essential for strong privacy, the large overhead from data-oblivious primitives in traditional SCA has hindered its practical adoption. Recent SCA variants that permit controlled leakages under differential privacy (DP) show a better balance between privacy and efficiency. However, they still face significant challenges, such as potentially unbounded privacy loss, suboptimal query planning, and lossy processing. To address these challenges, we introduce SPECIAL, the first SCA system that simultaneously ensures bounded privacy loss, advanced query planning, and lossless processing. SPECIAL employs a novel synopsis-assisted secure processing model, where a one-time privacy cost is spent to acquire private synopses (table statistics) from owner data. These synopses then allow SPECIAL to estimate (compaction) sizes for secure operations (e.g., filter, join) and index encrypted data without extra privacy loss. Crucially, these estimates and indexes can be prepared before runtime, thereby facilitating efficient query planning and accurate cost estimations. Moreover, by using one-sided noise mechanisms and private upper bound techniques, SPECIAL ensures strict lossless processing for complex queries (e.g., multi-join). Through a comprehensive benchmark, we show that SPECIAL significantly outperforms cutting-edge SCAs, with up to 80X faster query times and over 900X smaller memory for complex queries. Moreover, it also achieves up to an 89X reduction in privacy loss under continual processing.

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References (82)
  1. [n.d.]. Financial Dataset. https://relational-data.org/dataset/Financial. Accessed: 2024-03-30.
  2. Two Can Keep A Secret: A Distributed Architecture for Secure Database Services.. In CIDR, Vol. 2005. 186–199.
  3. An 0 (n log n) sorting network. In Proceedings of the fifteenth annual ACM symposium on Theory of computing. 1–9.
  4. Dynamic volume-hiding encrypted multi-maps with applications to searchable encryption. Cryptology ePrint Archive (2021).
  5. High-throughput semi-honest secure three-party computation with an honest majority. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 805–817.
  6. Bucket oblivious sort: An extremely simple oblivious sort. In Symposium on Simplicity in Algorithms. SIAM, 8–14.
  7. Kenneth E Batcher. 1968. Sorting networks and their applications. In Proceedings of the April 30–May 2, 1968, spring joint computer conference. 307–314.
  8. SMCQL: Secure Query Processing for Private Data Networks. Proc. VLDB Endow. 10, 6 (2017), 673–684.
  9. Shrinkwrap: efficient sql query processing in differentially private data federations. Proceedings of the VLDB Endowment 12, 3 (2018).
  10. Saqe: practical privacy-preserving approximate query processing for data federations. Proceedings of the VLDB Endowment 13, 12 (2020), 2691–2705.
  11. Completeness theorems for non-cryptographic fault-tolerant distributed computation. In Providing sound foundations for cryptography: on the work of Shafi Goldwasser and Silvio Micali. 351–371.
  12. Revisiting leakage abuse attacks. Cryptology ePrint Archive (2019).
  13. System R: An architectural overview. IBM systems journal 20, 1 (1981), 41–62.
  14. Epsolute: E iciently erying Databases While Providing Differential Privacy. (2021).
  15. Mark Bun and Thomas Steinke. 2016. Concentrated differential privacy: Simplifications, extensions, and lower bounds. In Theory of Cryptography Conference. Springer, 635–658.
  16. Leakage-abuse attacks against searchable encryption. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 668–679.
  17. Foundations of differentially oblivious algorithms. ACM Journal of the ACM (JACM) 69, 4 (2022), 1–49.
  18. Towards Practical Oblivious Join. In Proceedings of the 2022 International Conference on Management of Data. 803–817.
  19. Differentially private access patterns for searchable symmetric encryption. In IEEE INFOCOM 2018-IEEE conference on computer communications. IEEE, 810–818.
  20. Differentially oblivious database joins: Overcoming the worst-case curse of fully oblivious algorithms. Cryptology ePrint Archive (2021).
  21. Obladi: Oblivious serializable transactions in the cloud. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). 727–743.
  22. R2t: Instance-optimal truncation for differentially private query evaluation with foreign keys. In Proceedings of the 2022 International Conference on Management of Data. 759–772.
  23. Differential privacy under continual observation. In Proceedings of the forty-second ACM symposium on Theory of computing. 715–724.
  24. The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9, 3-4 (2014), 211–407.
  25. Fundamentals of Database Systems¡/Title. In Advances in Databases and Information Systems: 24th European Conference, ADBIS 2020, Lyon, France, August 25–27, 2020, Proceedings, Vol. 12245. Springer Nature, 139.
  26. Saba Eskandarian and Matei Zaharia. 2017. Oblidb: Oblivious query processing for secure databases. arXiv preprint arXiv:1710.00458 (2017).
  27. Oded Goldreich. 2004. Foundations of Cryptography, Volume 2. Cambridge university press Cambridge.
  28. Oded Goldreich. 2009. Foundations of cryptography: volume 2, basic applications. Cambridge university press.
  29. Michael T Goodrich. 2014. Zig-zag sort: A simple deterministic data-oblivious sorting algorithm running in o (n log n) time. In Proceedings of the forty-sixth annual ACM symposium on Theory of computing. 684–693.
  30. Cheaper private set intersection via differentially private leakage. Proceedings on Privacy Enhancing Technologies 2019, 3 (2019).
  31. Pump up the volume: Practical database reconstruction from volume leakage on range queries. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 315–331.
  32. Cardinality estimation in DBMS: A comprehensive benchmark evaluation. arXiv preprint arXiv:2109.05877 (2021).
  33. Hazar Harmouch and Felix Naumann. 2017. Cardinality estimation: An experimental survey. Proceedings of the VLDB Endowment 11, 4 (2017), 499–512.
  34. Composing differential privacy and secure computation: A case study on scaling private record linkage. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 1389–1406.
  35. Sdb: A secure query processing system with data interoperability. Proceedings of the VLDB Endowment 8, 12 (2015), 1876–1879.
  36. Simplicity Done Right for Join Ordering.. In CIDR.
  37. TiDB: a Raft-based HTAP database. Proceedings of the VLDB Endowment 13, 12 (2020), 3072–3084.
  38. Secure multi-party sorting and applications. Cryptology ePrint Archive (2011).
  39. Outsourcing multi-party computation. Cryptology ePrint Archive (2011).
  40. Generic attacks on secure outsourced databases. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 1329–1340.
  41. Marcel Keller and Peter Scholl. 2014. Efficient, oblivious data structures for MPC. In Advances in Cryptology–ASIACRYPT 2014: 20th International Conference on the Theory and Application of Cryptology and Information Security, Kaoshiung, Taiwan, ROC, December 7-11, 2014, Proceedings, Part II 20. Springer, 506–525.
  42. Daniel Kifer and Ashwin Machanavajjhala. 2011. No free lunch in data privacy. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. 193–204.
  43. Crypten: Secure multi-party computation meets machine learning. Advances in Neural Information Processing Systems 34 (2021), 4961–4973.
  44. Privatesql: a differentially private sql query engine. Proceedings of the VLDB Endowment 12, 11 (2019), 1371–1384.
  45. The case for learned index structures. In Proceedings of the 2018 international conference on management of data. 489–504.
  46. {{\{{SECRECY}}\}}: Secure collaborative analytics in untrusted clouds. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23). 1031–1056.
  47. Lovingmage. 2024. Synopsis Assisted Secure Collaborative Analytics. https://github.com/lovingmage/SPECIAL/.
  48. How to play any mental game. In Proceedings of the Nineteenth ACM Symp. on Theory of Computing, STOC. ACM New York, NY, USA, 218–229.
  49. Computational differential privacy. In Annual International Cryptology Conference. Springer, 126–142.
  50. Oblix: An efficient oblivious search index. In 2018 IEEE symposium on security and privacy (SP). IEEE, 279–296.
  51. Payman Mohassel and Yupeng Zhang. 2017. Secureml: A system for scalable privacy-preserving machine learning. In 2017 IEEE symposium on security and privacy (SP). IEEE, 19–38.
  52. Delegated Private Matching for Compute. Proceedings on Privacy Enhancing Technologies (2024).
  53. Simon Oya and Florian Kerschbaum. 2021. Hiding the access pattern is not enough: Exploiting search pattern leakage in searchable encryption. In 30th USENIX security symposium (USENIX Security 21). 127–142.
  54. Mitigating leakage in secure cloud-hosted data structures: Volume-hiding for multi-maps via hashing. In Proceedings of the 2019 ACM SIGSAC conference on computer and communications security. 79–93.
  55. Martin Pettai and Peeter Laud. 2015. Combining differential privacy and secure multiparty computation. In Proceedings of the 31st annual computer security applications conference. 421–430.
  56. Benny Pinkas and Tzachy Reinman. 2010. Oblivious RAM revisited. In Advances in Cryptology–CRYPTO 2010: 30th Annual Cryptology Conference, Santa Barbara, CA, USA, August 15-19, 2010. Proceedings 30. Springer, 502–519.
  57. Senate: a {{\{{Maliciously-Secure}}\}}{{\{{MPC}}\}} platform for collaborative analytics. In 30th USENIX Security Symposium (USENIX Security 21). 2129–2146.
  58. Adore: Differentially oblivious relational database operators. arXiv preprint arXiv:2212.05176 (2022).
  59. Doquet: Differentially Oblivious Range and Join Queries with Private Data Structures. ([n. d.]).
  60. Vijaya Ramachandran and Elaine Shi. 2021. Data oblivious algorithms for multicores. In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures. 373–384.
  61. Crypte: Crypto-assisted differential privacy on untrusted servers. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 603–619.
  62. Fast Fully Oblivious Compaction and Shuffling. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. 2565–2579.
  63. When it’s all just too much: outsourcing MPC-preprocessing. In Cryptography and Coding: 16th IMA International Conference, IMACC 2017, Oxford, UK, December 12-14, 2017, Proceedings 16. Springer, 77–99.
  64. Obfuscated access and search patterns in searchable encryption. arXiv preprint arXiv:2102.09651 (2021).
  65. Database system concepts. (2011).
  66. Towards practical oblivious RAM. arXiv preprint arXiv:1106.3652 (2011).
  67. CryptGPU: Fast privacy-preserving machine learning on the GPU. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 1021–1038.
  68. {{\{{EnigMap}}\}}:{{\{{External-Memory}}\}} Oblivious Map for Secure Enclaves. In 32nd USENIX Security Symposium (USENIX Security 23). 4033–4050.
  69. Conclave: secure multi-party computation on big data. In Proceedings of the Fourteenth EuroSys Conference 2019. 1–18.
  70. Differentially private oblivious ram. arXiv preprint arXiv:1601.03378 (2016).
  71. DP-cryptography: marrying differential privacy and cryptography in emerging applications. Commun. ACM 64, 2 (2021), 84–93.
  72. DP-Sync: Hiding update patterns in secure outsourced databases with differential privacy. In Proceedings of the 2021 International Conference on Management of Data. 1892–1905.
  73. IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy. arXiv preprint arXiv:2203.05084 (2022).
  74. Private Proof-of-Stake Blockchains using Differentially-private Stake Distortion. Cryptology ePrint Archive (2023).
  75. Oblivious data structures. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. 215–226.
  76. Yilei Wang and Ke Yi. 2021. Secure yannakakis: Join-aggregate queries over private data. In Proceedings of the 2021 International Conference on Management of Data. 1969–1981.
  77. Yonghui Xiao and Li Xiong. 2015. Protecting locations with differential privacy under temporal correlations. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 1298–1309.
  78. Andrew Chi-Chih Yao. 1986. How to generate and exchange secrets. In 27th annual symposium on foundations of computer science (Sfcs 1986). IEEE, 162–167.
  79. Longshot: Indexing Growing Databases Using MPC and Differential Privacy. Proceedings of the VLDB Endowment 16, 8 (2023), 2005–2018.
  80. All your queries are belong to us: the power of {{\{{File-Injection}}\}} attacks on searchable encryption. In 25th USENIX Security Symposium (USENIX Security 16). 707–720.
  81. {{\{{PrivSyn}}\}}: Differentially Private Data Synthesis. In 30th USENIX Security Symposium (USENIX Security 21). 929–946.
  82. Opaque: An oblivious and encrypted distributed analytics platform. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). 283–298.
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