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A Privacy-Preserving Index for Range Queries

Bijit Hore, Sharad Mehrotra, Gene Tsudik Keiichi Shimamura. A Privacy-Preserving Index for Range Queries. Background. Rise in use of cloud services Outsourcing of IT infrastructure Increasing use of Database As a Service (DAS). Database as a Service. Data is stored at service provider

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A Privacy-Preserving Index for Range Queries

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  1. Bijit Hore, Sharad Mehrotra, Gene Tsudik Keiichi Shimamura A Privacy-Preserving Index for Range Queries

  2. Background • Rise in use of cloud services • Outsourcing of IT infrastructure • Increasing use of Database As a Service (DAS)

  3. Database as a Service • Data is stored at service provider • Service provider cannot be trusted • Security perimeter around data owner • Client is secure and trusted • Server (service provider) is not trusted

  4. Problem • How to maintain security and privacy using DAS? • How to estimate and analyze the effectiveness of the solution?

  5. Solution • Split the query into two parts • Insecure query that runs on the server • Secure query that runs on the client • Bucketization for range queries

  6. Encryption and Bucketization

  7. Tradeoff • Larger buckets → more privacy • Smaller buckets → more performance • Want: maximum privacy and performance • Reality: tradeoff between privacy and performance

  8. Optimizing Buckets for Performance

  9. Breaking Bucketization • With knowledge of • Bucketization scheme • Probability distribution in each bucket • the attacker can form statistical estimates of the values of attributes used in bucketization

  10. Protecting Against Attacks • Increase variance of values in a bucket • More different values in each bucket weakens statistical estimates • Increasing variance of one bucket lowers the variance of others • Add entropy • More values in each bucket weakens statistical estimates • More rows are returned per bucket, decreasing performance

  11. Variance and Entropy

  12. Compromise • Maximize variance and entropy for most privacy • Specify a maximum performance degradation • Redistribute elements from “optimized buckets” to “composite buckets”

  13. Diffusion

  14. Precision Results

  15. Variance Results

  16. Entropy Results

  17. Privacy vs. Performance

  18. Conclusion • Tradeoff between privacy and performance • Provides a solution for range queries that • Maximizes privacy • Limits performance degradation

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