Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Date
2012-02Permanent link to this record
http://hdl.handle.net/10754/562069
Metadata
Show full item recordAbstract
This paper considers a cloud computing setting in which similarity querying of metric data is outsourced to a service provider. The data is to be revealed only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example. Outsourcing offers the data owner scalability and a low-initial investment. The need for privacy may be due to the data being sensitive (e.g., in medicine), valuable (e.g., in astronomy), or otherwise confidential. Given this setting, the paper presents techniques that transform the data prior to supplying it to the service provider for similarity queries on the transformed data. Our techniques provide interesting trade-offs between query cost and accuracy. They are then further extended to offer an intuitive privacy guarantee. Empirical studies with real data demonstrate that the techniques are capable of offering privacy while enabling efficient and accurate processing of similarity queries.Citation
Yiu, M. L., Assent, I., Jensen, C. S., & Kalnis, P. (2012). Outsourced Similarity Search on Metric Data Assets. IEEE Transactions on Knowledge and Data Engineering, 24(2), 338–352. doi:10.1109/tkde.2010.222Additional Links
http://ieeexplore.ieee.org/document/5620912/http://www4.comp.polyu.edu.hk/%7Ecsmlyiu/journal/TKDE_metricpriv.pdf
ae974a485f413a2113503eed53cd6c53
10.1109/TKDE.2010.222