Privacy Preserving Record Matching Using Automated Semi-trusted Broker
In this paper, we present a novel scheme that allows multiple data publishers that continuously generate new data and periodically update existing data, to share sensitive individual records with multiple data subscribers while protecting the privacy of their clients. An example of such sharing is that of health care providers sharing patients’ records with clinical researchers. Traditionally, such sharing is performed by sanitizing personally identifying information from individual records. However, removing identifying information prevents any updates to the source information to be easily propagated to the sanitized records, or sanitized records belonging to the same client to be linked together. We solve this problem by utilizing the services of a third party, which is of very limited capabilities in terms of its abilities to keep a secret, secret, and by encrypting the identification part used to link individual records with different keys. The scheme is based on strong security primitives that do not require shared encryption keys.
This work was partially supported by the U.S. National Science Foundation under Grant No. 0905232, and by Colorado State University under an internal research grant.
- 2.Curtmola, R., Garay, J.A., Kamara, S., Ostrovsky, R.: Searchable symmetric encryption: improved definitions and efficient constructions. In: Proceedings of the 13th ACM Conference on Computer and Communications Security, Alexandria, VA, USA. pp. 79–88 (2006)Google Scholar
- 3.Moataz, T., Shikfa, A.: Boolean symmetric searchable encryption. In: Proceedings of the 8th ACM Symposium on Information, Computer and Communications Security, Hangzhou, China. pp. 265–276 (2013)Google Scholar
- 5.Stefanov, E., van Dijk, M., Shi, E., Fletcher, C.W., Ren, L., Yu, X., Devadas, S.: Path ORAM: an extremely simple oblivious RAM protocol. In: Proceedings of ACM Conference on Computer and Communications Security, Berlin, Germany. 299–310 (2013)Google Scholar
- 8.Kamara, S., Mohassel, P., Raykova, M., Sadeghian, S.: Scaling private set intersection to billion-element sets. In: Christin, N., Safavi-Naini, R. (eds.) FC 2014. LNCS, vol. 8437, pp. 193–213. Springer, Heidelberg (2014) Google Scholar
- 9.Goldreich, O.: Secure multi-party computation. Manuscript. Preliminary version (1998). http://citeseerx.ist.psu.edu. Accessed on 30 April 2015
- 10.Agrawal, R., Evfimievski, A., Srikant, R.: Information sharing across private databases. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, San Diego, CA, USA. pp. 86–97 (2003)Google Scholar
- 12.Boyd, A.D., Saxman, P.R., Hunscher, D.A., Smith, K.A., Morris, T.D., Kaston, M., Bayoff, F., Rogers, B., Hayes, P., Rajeev, N., Kline-Rogers, E., Eagle, K., Clauw, D., Greden, J.F., Green, L.A., Athey, B.D.: The University of Michigan honest broker: a web-based service for clinical and translational research and practice. J. Am. Med. Inform. Assoc. : JAMIA 16, 784–791 (2009)CrossRefGoogle Scholar
- 14.Jefferies, N., Mitchell, C.J., Walker, M.: A proposed architecture for trusted third party services. In: Proceedings of the International Conference on Cryptography: Policy and Algorithms, Brisbane, Queensland, Australia. pp. 98–104 (1995)Google Scholar
- 15.Ajmani, S., Morris, R., Liskov, B.: A trusted third-party computation service (2001). http://www.pmg.lcs.mit.edu/~ajmani/papers/tep.ps. Accessed on 30 April 2015
- 18.Chow, S.S., Lee, J.H., Subramanian, L.: Two-party computation model for privacy-preserving queries over distributed databases. In: Proceedings of the 2009 Network and Distributed System Security Symposium, San Diego, CA, USA (2009)Google Scholar