Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3448016.3457544acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
tutorial
Open access

Practical Security and Privacy for Database Systems

Published: 18 June 2021 Publication History

Abstract

Computing technology has enabled massive digital traces of our personal lives to be collected and stored. These datasets play an important role in numerous real-life applications and research analysis, such as contact tracing for COVID 19, but they contain sensitive information about individuals. When managing these datasets, privacy is usually addressed as an afterthought, engineered on top of a database system optimized for performance and usability. This has led to a plethora of unexpected privacy attacks in the news. Specialized privacy-preserving solutions usually require a group of privacy experts and they are not directly transferable to other domains. There is an urgent need for a generally trustworthy database system that offers end-to-end security and privacy guarantees. In this tutorial, we will first describe the security and privacy requirements for database systems in different settings and cover the state-of-the-art tools that achieve these requirements. We will also show challenges in integrating these techniques together and demonstrate the design principles and optimization opportunities for these security and privacy-aware database systems. This is designed to be a three hour tutorial.

References

[1]
A. Agarwal, M. Herlihy, S. Kamara, and T. Moataz. Encrypted databases for differential privacy. Proceedings on Privacy Enhancing Technologies, 2019(3):170 -- 190, 01 Jul. 2019.
[2]
G. Aggarwal, M. Bawa, P. Ganesan, H. Garcia-Molina, K. Kenthapadi, R. Motwani, U. Srivastava, D. Thomas, and Y. Xu. Two can keep a secret: A distributed architecture for secure database services. CIDR, 2005.
[3]
L. Allen, P. Antonopoulos, A. Arasu, J. Gehrke, J. Hammer, J. Hunter, R. Kaushik, D. Kossmann, J. Lee, R. Ramamurthy, et al. Veritas: shared verifiable databases and tables in the cloud. In 9th Biennial Conference on Innovative Data Systems Research (CIDR), 2019.
[4]
Apple. Apple Secure Enclave. https://support.apple.com/guide/security/secure-enclave-overview-sec59b0b31ff/web.
[5]
A. Arasu, S. Blanas, K. Eguro, R. Kaushik, D. Kossmann, R. Ramamurthy, and R. Venkatesan. Orthogonal Security with Cipherbase. In CIDR. Citeseer, 2013.
[6]
A. Arasu, K. Eguro, R. Kaushik, and R. Ramamurthy. Querying encrypted data (tutorial). In 29th International Conference on Data Engineering (ICDE), April 2013. Tutorial presentation.
[7]
A. Arasu and R. Kaushik. Oblivious query processing. ICDT, 2014.
[8]
J. Bater, G. Elliott, C. Eggen, S. Goel, A. Kho, and J. Rogers. SMCQL: Secure Querying for Federated Databases. Proceedings of the VLDB Endowment, 10, 2017.
[9]
J. Bater, X. He, W. Ehrich, A. Machanavajjhala, and J. Rogers. Shrinkwrap: Differentially-Private Query Processing in Private Data Federations. Proceedings of the VLDB Endowment, 12(3):307--320, 2019.
[10]
J. Bater, Y. Park, X. He, X. Wang, and J. Rogers. Saqe: Practical privacy-preserving approximate query processing for data federations. Proc. VLDB Endow., 2020.
[11]
M. Benedikt, J. Leblay, and E. Tsamoura. Querying with access patterns and integrity constraints. Proc. VLDB Endow., 8(6), Feb. 2015.
[12]
D. Bogdanov, L. Kamm, B. Kubo, R. Rebane, V. Sokk, and R. Talviste. Students and Taxes: A Privacy-Preserving Social Study Using Secure Computation. In Privacy Enhancing Technologies Symposium (PETS), 2016.
[13]
P. Bogetoft, D. L. Christensen, I. Damgård, M. Geisler, T. Jakobsen, M. Krøigaard, J. D. Nielsen, J. B. Nielsen, K. Nielsen, J. Pagter, M. I. Schwartzbach, and T. Toft. Secure multiparty computation goes live. In R. Dingledine and P. Golle, editors, FC 2009, volume 5628 of LNCS, Accra Beach, Barbados, Feb. 23--26, 2009. Springer, Heidelberg, Germany.
[14]
E. Boyle, N. Gilboa, and Y. Ishai. Function secret sharing. In E. Oswald and M. Fischlin, editors, EUROCRYPT 2015, Part II, volume 9057 of LNCS, pages 337--367, Sofia, Bulgaria, Apr. 26--30, 2015. Springer, Heidelberg, Germany.
[15]
R. Canetti. Universally composable security: A tutorial, 2016. Talk at Boston University, A Modular Approach to Cloud Security.
[16]
K. Chaudhuri and A. Sarwate. Differential privacy for signal processing and machine learning. WIFS'14, 2014.
[17]
B. Chor, N. Gilboa, and M. Naor. Private information retrieval by keywords. Citeseer, 1997.
[18]
B. Chor, O. Goldreich, E. Kushilevitz, and M. Sudan. Private information retrieval. In 36th FOCS, pages 41--50, Milwaukee, Wisconsin, Oct. 23--25, 1995. IEEE Computer Society Press.
[19]
G. Cormode. Building blocks of privacy: Differentially private mechanisms, 2013. Invited tutorial talk at Privacy Preserving Data Publication and Analysis (PrivDB) workshop.
[20]
H. Corrigan-Gibbs and D. Kogan. Private information retrieval with sublinear online time. In V. Rijmen and Y. Ishai, editors, EUROCRYPT 2020, Part I, LNCS, pages 44--75. Springer, Heidelberg, Germany, May 2020.
[21]
V. Costan and S. Devadas. Intel sgx explained. IACR Cryptol. ePrint Arch., 2016(86):1--118, 2016.
[22]
E. Crockett, C. Peikert, and C. Sharp. Alchemy: A language and compiler for homomorphic encryption made easy. In CCS, 2018.
[23]
N. Crooks, M. Burke, E. Cecchetti, S. Harel, R. Agarwal, and L. Alvisi. Obladi: Oblivious serializable transactions in the cloud. In 13th $$USENIX$$ Symposium on Operating Systems Design and Implementation ($$OSDI$$ 18), pages 727--743, 2018.
[24]
G. G. Dagher, B. C. M. Fung, N. Mohammed, and J. Clark. Secdm: privacy-preserving data outsourcing framework with differential privacy. Knowl. Inf. Syst., 62(5):1923--1960, 2020.
[25]
G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels. Dynamo: amazon's highly available key-value store. ACM SIGOPS operating systems review, 41(6):205--220, 2007.
[26]
C. Dwork. Differential privacy. International Colloquium on Automata, Languages and Programming, pages 1--12, 2006.
[27]
C. Dwork. Differential privacy. In Proceedings of the 33rd International Conference on Automata, Languages and Programming - Volume Part II, ICALP'06, pages 1--12, Berlin, Heidelberg, 2006. Springer-Verlag.
[28]
C. Dwork and A. Roth. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3--4):211--407, 2014.
[29]
M. El-Hindi, M. Heyden, C. Binnig, R. Ramamurthy, A. Arasu, and D. Kossmann. Blockchaindb-towards a shared database on blockchains. In Proceedings of the 2019 International Conference on Management of Data, pages 1905--1908. ACM, 2019.
[30]
S. Eskandarian and M. Zaharia. Oblidb: Oblivious query processing using hardware enclaves. arXiv preprint arXiv:1710.00458, 2017.
[31]
D. Froelicher, J. R. Troncoso-Pastoriza, J. S. Sousa, and J.-P. Hubaux. Drynx: Decentralized, secure, verifiable system for statistical queries and machine learning on distributed datasets. IEEE Transactions on Information Forensics and Security, 15:3035--3050, 2020.
[32]
O. Goldreich, S. Micali, and A. Wigderson. Proofs that yield nothing but their validity or all languages in NP have zero-knowledge proof systems. Journal of the ACM, 38(3):691--729, 1991.
[33]
S. Goldwasser, S. Micali, and C. Rackoff. The knowledge complexity of interactive proof-systems (extended abstract). In 17th ACM STOC, pages 291--304, Providence, RI, USA, May 6--8, 1985. ACM Press.
[34]
A. Gribov, D. Vinayagamurthy, and S. Gorbunov. Stealthdb: a scalable encrypted database with full sql query support. arXiv preprint arXiv:1711.02279, 2017.
[35]
P. Grubbs, M.-S. Lacharité, B. Minaud, and K. G. Paterson. Learning to Reconstruct: Statistical Learning Theory and Encrypted Database Attacks. In Learning to Reconstruct: Statistical Learning Theory and Encrypted Database Attacks, page 0. IEEE.
[36]
P. Grubbs, K. Sekniqi, V. Bindschaedler, M. Naveed, and T. Ristenpart. Leakage-abuse attacks against order-revealing encryption. pages 655--672, 2017.
[37]
D. Gupta, B. Mood, J. Feigenbaum, K. Butler, and P. Traynor. Using Intel Software Guard Extensions for Efficient Two-Party Secure Function Evaluation. 4th Workshop on Encrypted Computing and Applied Homomorphic Cryptography - WAHC'16, (February), 2016.
[38]
A. Haeberlen, B. C. Pierce, and A. Narayan. Differential privacy under fire. In 20th USENIX Security Symposium, San Francisco, CA, USA, August 8--12, 2011, Proceedings. USENIX Association, 2011.
[39]
M. Hay, K. Liu, G. Miklau, J. Pei, and E. Terzi. Privacy-aware data management in information networks. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, SIGMOD '11, page 1201--1204, New York, NY, USA, 2011. Association for Computing Machinery.
[40]
X. He, A. Machanavajjhala, C. Flynn, and D. Srivastava. 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, CCS '17, page 1389--1406, New York, NY, USA, 2017. Association for Computing Machinery.
[41]
R. Henry. Tutorial: Private information retrieval. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS '17, page 2611--2612, New York, NY, USA, 2017. Association for Computing Machinery.
[42]
N. Johnson, J. P. Near, and D. Song. Towards practical differential privacy for sql queries. Proc. VLDB Endow., 11(5):526--539, Jan. 2018.
[43]
G. Kellaris, G. Kollios, K. Nissim, and A. O'Neill. Generic attacks on secure outsourced databases. In CCS, pages 1329--1340. ACM, 2016.
[44]
E. M. Kornaropoulos, C. Papamanthou, and R. Tamassia. The state of the uniform: attacks on encrypted databases beyond the uniform query distribution. In 2020 IEEE Symposium on Security and Privacy (SP), pages 1223--1240. IEEE, 2020.
[45]
I. Kotsogiannis, Y. Tau, X. He, M. Fanaeepour, A. Machanavajjhala, M. Hay, and G. Miklau. PrivateSQL: A Differentially Private SQL Engine. Proceedings of the VLDB Endowment, 12(12), 2019.
[46]
E. Kushilevitz and R. Ostrovsky. Replication is NOT needed: SINGLE database, computationally-private information retrieval. In 38th FOCS, pages 364--373, Miami Beach, Florida, Oct. 19--22, 1997. IEEE Computer Society Press.
[47]
K. Lefevre, D. J. DeWitt, and R. Ramakrishnan. Incognito: Efficient full-domain k-anonymity. In SIGMOD, pages 49--60. ACM, 2005.
[48]
T. Lepoint, S. Patel, M. Raykova, K. Seth, and N. Trieu. Private join and compute from pir with default. Cryptology ePrint Archive, Report 2020/1011, 2020. https://eprint.iacr.org/2020/1011.
[49]
Y. Lindell. How to simulate it: A tutorial on the simulation proof technique, 2018. Tutorials on the Foundations of Cryptography.
[50]
C. Liu, X. S. Wang, K. Nayak, Y. Huang, and E. Shi. ObliVM : A Programming Framework for Secure Computation. Oakland, pages 359--376, 2015.
[51]
K. Liu, G. Miklau, J. Pei, and E. Terzi. Privacy-aware data mining in information networks. KDD 2010 Tutorial, 2010.
[52]
A. Machanavajjhala, X. He, and M. Hay. Differential privacy in the wild: A tutorial on current practices and open challenges. In Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD '17, page 1727--1730, New York, NY, USA, 2017. Association for Computing Machinery.
[53]
A. Machanavajjhala, D. Kifer, J. Abowd, J. Gehrke, and L. Vilhuber. Privacy: Theory meets practice on the map. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE '08, page 277--286, USA, 2008. IEEE Computer Society.
[54]
S. Maiyya, V. Zakhary, M. J. Amiri, D. Agrawal, and A. El Abbadi. Database and distributed computing foundations of blockchains. In Proceedings of the 2019 International Conference on Management of Data, SIGMOD '19, page 2036--2041, New York, NY, USA, 2019. Association for Computing Machinery.
[55]
F. D. McSherry. Privacy integrated queries: An extensible platform for privacy-preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD '09. ACM, 2009.
[56]
I. Mironov, O. Pandey, O. Reingold, and S. Vadhan. Computational differential privacy. In CRYPTO, pages 126--142. Springer, 2009.
[57]
P. Mohassel, P. Rindal, and M. Rosulek. Fast database joins and psi for secret shared data. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, CCS '20, page 1271--1287, New York, NY, USA, 2020. Association for Computing Machinery.
[58]
A. Narayan and A. Haeberlen. DJoin: differentially private join queries over distributed databases. Proceedings of the 10th USENIX Symposium łdots, page 14, 2012.
[59]
M. Naveed, S. Kamara, and C. V. Wright. Inference attacks on property-preserving encrypted databases. In I. Ray, N. Li, and C. Kruegel, editors, ACM CCS 2015, pages 644--655, Denver, CO, USA, Oct. 12--16, 2015. ACM Press.
[60]
M. Naveed, C. V. Wright, S. Kamara, and C. V. Wright. Inference Attacks on Property-Preserving Encrypted Databases. In CCS, pages 644--655. ACM, 2015.
[61]
F. Olumofin and I. Goldberg. Privacy-preserving queries over relational databases. In International Symposium on Privacy Enhancing Technologies Symposium, pages 75--92. Springer, 2010.
[62]
R. A. Popa, C. M. S. Redfield, N. Zeldovich, and H. Balakrishnan. Cryptdb: Protecting confidentiality with encrypted query processing. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles, SOSP '11, page 85--100, New York, NY, USA, 2011. Association for Computing Machinery.
[63]
C. Priebe, K. Vaswani, and M. Costa. EnclaveDB: A Secure Database using SGX. In EnclaveDB: A Secure Database using SGX, page 0. IEEE, 2018.
[64]
A. Rajan, L. Qin, D. W. Archer, D. Boneh, T. Lepoint, and M. Varia. Callisto: A cryptographic approach to detecting serial perpetrators of sexual misconduct. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies, page 49. ACM, 2018.
[65]
M. Rosulek. A brief history of practical garbled circuit optimizations, 2015. Talk at Simons Secure Computation Workshop.
[66]
I. Roy, S. T. V. Setty, A. Kilzer, V. Shmatikov, and E. Witchel. Airavat: Security and privacy for mapreduce. In Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation, NSDI'10, page 20, USA, 2010. USENIX Association.
[67]
A. Roy Chowdhury, C. Wang, X. He, A. Machanavajjhala, and S. Jha. Crypte: Crypto-assisted differential privacy on untrusted servers. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD '20, New York, NY, USA, 2020. Association for Computing Machinery.
[68]
A. Roy Chowdhury, C. Wang, X. He, A. Machanavajjhala, and S. Jha. Cryptε: Crypto-assisted differential privacy on untrusted servers. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pages 603--619, 2020.
[69]
M. Sabt, M. Achemlal, and A. Bouabdallah. Trusted execution environment: what it is, and what it is not. In 2015 IEEE Trustcom/BigDataSE/ISPA, volume 1, pages 57--64. IEEE, 2015.
[70]
S. Sasy, S. Gorbunov, and C. Fletcher. ZeroTrace: Oblivious memory primitives from Intel SGX. IACR Cryptology ?Archive Report, 549:2017, 2017.
[71]
S. Sharma, A. Burtsev, and S. Mehrotra. Advances in cryptography and secure hardware for data outsourcing. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pages 1798--1801, 2020.
[72]
M. Suresh, Z. She, W. Wallace, A. Lahlou, and J. Rogers. Kloakdb: A platform for analyzing sensitive data with k-anonymous query processing. CoRR, abs/1904.00411, 2019.
[73]
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 26th $$USENIX$$ Security Symposium ($$USENIX$$ Security 17), pages 1041--1056, 2017.
[74]
N. Volgushev, M. Schwarzkopf, B. Getchell, M. Varia, A. Lapets, and A. Bestavros. Conclave: Secure multi-party computation on big data. In EuroSys, 2019.
[75]
F. Wang, C. Yun, S. Goldwasser, V. Vaikuntanathan, and M. Zaharia. Splinter: Practical Private Queries on Public Data. In NSDI, pages 299--313, 2017.
[76]
W. Wang, G. Chen, X. Pan, Y. Zhang, X. Wang, V. Bindschaedler, H. Tang, and C. A. Gunter. Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGX. CCS, pages 2421--2434, 2017.
[77]
X. Wang, S. Ranellucci, and J. Katz. Authenticated garbling and efficient maliciously secure two-party computation. In CCS, 2017.
[78]
Z. Wei, U. Leck, and S. Link. Entity integrity, referential integrity, and query optimization with embedded uniqueness constraints. In ICDE, 2019.
[79]
W. K. Wong, B. Kao, D. W. L. Cheung, R. Li, and S. M. Yiu. Secure query processing with data interoperability in a cloud database environment. In SIGMOD, pages 1395--1406. ACM, 2014.
[80]
B. P. Y. Lindell. Secure computation and efficiency, 2011. Invited talk at Bar-Ilan Winter School.
[81]
Y. Yang, Z. Zhang, G. Miklau, M. Winslett, and X. Xiao. Differential privacy in data publication and analysis. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD '12, page 601--606, New York, NY, USA, 2012. Association for Computing Machinery.
[82]
A. C.-C. Yao. How to generate and exchange secrets (extended abstract). In 27th FOCS, pages 162--167, Toronto, Ontario, Canada, Oct. 27--29, 1986. IEEE Computer Society Press.
[83]
D. Zhang, R. McKenna, I. Kotsogiannis, M. Hay, A. Machanavajjhala, and G. Miklau. EKTELO: A framework for defining differentially-private computations. In G. Das, C. M. Jermaine, and P. A. Bernstein, editors, Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, TX, USA, June 10--15, 2018, pages 115--130. ACM, 2018.
[84]
Y. Zhang, D. Genkin, J. Katz, D. Papadopoulos, and C. Papamanthou. vSQL: Verifying arbitrary SQL queries over dynamic outsourced databases. Cryptology ePrint Archive, Report 2017/1145, 2017. https://eprint.iacr.org/2017/1145.
[85]
Y. Zhang, J. Katz, and C. Papamanthou. IntegriDB: Verifiable SQL for Outsourced Databases. ACM CCS, 2015.
[86]
W. Zheng, A. Dave, J. G. Beekman, R. A. Popa, J. E. Gonzalez, and I. Stoica. Opaque: An oblivious and encrypted distributed analytics platform. In 14th $$USENIX$$ Symposium on Networked Systems Design and Implementation ($$NSDI$$ 17), pages 283--298, 2017.

Cited By

View all
  • (2022)Practical Volume-Hiding Encrypted Multi-Maps with Optimal Overhead and BeyondProceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security10.1145/3548606.3559345(2825-2839)Online publication date: 7-Nov-2022
  • (2021)A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 PandemicIEEE Access10.1109/ACCESS.2021.31306109(164159-164187)Online publication date: 2021

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data
June 2021
2969 pages
ISBN:9781450383431
DOI:10.1145/3448016
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. differential privacy
  2. privacy
  3. secure computation
  4. security
  5. trusted execution environment

Qualifiers

  • Tutorial

Funding Sources

Conference

SIGMOD/PODS '21
Sponsor:

Acceptance Rates

Overall Acceptance Rate 785 of 4,003 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)622
  • Downloads (Last 6 weeks)63
Reflects downloads up to 22 Sep 2024

Other Metrics

Citations

Cited By

View all
  • (2022)Practical Volume-Hiding Encrypted Multi-Maps with Optimal Overhead and BeyondProceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security10.1145/3548606.3559345(2825-2839)Online publication date: 7-Nov-2022
  • (2021)A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 PandemicIEEE Access10.1109/ACCESS.2021.31306109(164159-164187)Online publication date: 2021

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media