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Incremental Deterministic Public-Key Encryption

This research paper explores the concept of incremental deterministic public-key encryption, its security guarantees, and its applications in various scenarios. It also presents lower bounds, generic solutions, and constructions based on computational assumptions.

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Incremental Deterministic Public-Key Encryption

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  1. Ilya Mironov, Omkant Pandey, Omer Reingold, Gil Segev Microsoft Research Incremental Deterministic Public-Key Encryption

  2. Incremental Deterministic Public-Key Encryption

  3. Deterministic Public-Key Encryption

  4. -source adversary min-entropy min-entropy min-entropy : Probability of any output

  5. Deterministic Public-Key Encryption: PRIV1-IND Epk[ ] Epk[ ] min-entropy min-entropy and are independent of PK [Bellare, Boldyreva, O’Neill CRYPTO’07]

  6. Is It Secure? • search • de-duplication • deterministic KEM Secure Deterministic Encryption Computational assumptions Min-entropy of the source • Long, unpredictable plaintext: • digital photograph • MS Word document • entire database • full disk

  7. security Length of the plaintext efficiency

  8. Incrementality degree • Incrementality with access to plaintext: setting bit • Incrementality without access to plaintext: flipping bit

  9. Incremental Deterministic Public-Key Encryption

  10. Our results • Lower bound: • Two schemes • Generic Solution • DDH-based solution tight up to polylog factors incrementality Deterministic Encryption Incremental Deterministic Encryption min-entropy

  11. Naïve Generic Solution min-entropy ? … E E E E: deterministic encryption scheme

  12. Sample-then-extract min-entropy similar min-entropy rate [Nisan,Zuckerman’96] [Vadhan’04]

  13. Generic Solution min-entropy Partition input into random subsets PRIV-IND  PRIV1-IND with Incrementality

  14. Standard Model DDH  PRIV1-IND with Incrementality

  15. LossyTrapdoor Functions Injective mode: w/ trapdoor Lossy mode: [Peikert, Waters STOC’08]

  16. Smooth Trapdoor Functions Injective mode: w/ trapdoor Smooth mode: statisticallyclose min-entropy

  17. Smooth Trapdoor Functions  PRIV1-IND Security min-entropy min-entropy injective mode: smooth mode:

  18. Construction of PRIV1-IND Lossy Trapdoor Function Pairwise-independent permutation Smooth Trapdoor Function Deterministic Public-Key Encryption [Boldyreva, Fehr, O’Neill CRYPTO’08]

  19. Construction of PRIV1-IND Lossy Trapdoor Function Pairwise-independent permutation Smooth Trapdoor Function Incremental Deterministic Public-Key Encryption [Boldyreva, Fehr, O’Neill CRYPTO’08]

  20. Construction of Lossy TDF - group of order generated by • Sample • Outputand Key generation • Given output Encryption • Given compute • Output Decryption [Freeman, Goldreich, Kiltz, Rosen, Segev PKC’10] [Brakerski, Segev CRYPTO’11]

  21. Security Argument: Lossy TDF rank rank 1 — injective — bits

  22. Towards Incremental Smooth TDF rank sparse rank ℓ sparse — injective if has min-entropy , statistically close to the uniform over its range

  23. Towards Incremental Smooth TDF  Sample-then-extract + Leftover Hash Lemma

  24. Towards Incremental Smooth TDF

  25. Towards Incremental Smooth TDF

  26. Smooth vs Injective Mode rank full rank

  27. Incrementality

  28. Open Problems Incremental Deterministic Encryption: • Stronger security: PRIV-IND (multiple messages) • Length-preserving in the standard model Deterministic Encryption: • Relaxing the definition to allow dependency on the public key

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