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Privacy-Preserving Index: Balancing Range Query Privacy and Performance in Cloud Databases

As the use of Database as a Service (DAS) grows, the challenge lies in ensuring security and privacy. This research proposes a solution that splits queries, utilizes bucketization, and optimizes buckets to balance privacy and performance. By increasing variance and adding entropy, it strengthens defense against attacks while maintaining query efficiency. The proposed approach achieves a tradeoff between maximizing privacy and limiting performance degradation in range queries within cloud databases.

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Privacy-Preserving Index: Balancing Range Query Privacy and Performance in Cloud Databases

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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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