Femur: A Flexible Framework for Fast and Secure Querying from Public Key-Value Store
Summary: Introduces distance-based indistinguishability to obtain provable privacy–performance trade-offs for public key-value querying. Femur combines a space-efficient learned index, noise-based obfuscation of storage ranges, and a variable-range PIR to adaptively maximize throughput (up to 163.9x) under relaxed privacy. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jiaoyi Zhang
- 2. Liqiang Peng
- 3. Mo Sha
- 4. Weiran Liu
- 5. Xiang Li
- 6. Sheng Wang
- 7. Feifei Li
- 8. Mingyu Gao
- 9. Huanchen Zhang
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