On Public Key Encryption from Noisy Codewords Yuval Ishai Technion & UCLA Eli Ben-Sasson (Technion) Iddo Ben-Tov (Technion) Ivan Damgård (Aarhus) Noga.

Slides:



Advertisements
Similar presentations
Efficient Lattice (H)IBE in the standard model Shweta Agrawal, Dan Boneh, Xavier Boyen.
Advertisements

Impagliazzos Worlds in Arithmetic Complexity: A Progress Report Scott Aaronson and Andrew Drucker MIT 100% QUANTUM-FREE TALK (FROM COWS NOT TREATED WITH.
An Introduction to Randomness Extractors Ronen Shaltiel University of Haifa Daddy, how do computers get random bits?
Extracting Randomness From Few Independent Sources Boaz Barak, IAS Russell Impagliazzo, UCSD Avi Wigderson, IAS.
Efficiency vs. Assumptions in Secure Computation Yuval Ishai Technion & UCLA.
Quantum Money from Hidden Subspaces Scott Aaronson and Paul Christiano.
Computational Privacy. Overview Goal: Allow n-private computation of arbitrary funcs. –Impossible in information-theoretic setting Computational setting:
Approximate List- Decoding and Hardness Amplification Valentine Kabanets (SFU) joint work with Russell Impagliazzo and Ragesh Jaiswal (UCSD)
Paper by: Craig Gentry Presented By: Daniel Henneberger.
On Combinatorial vs Algebraic Computational Problems Boaz Barak – MSR New England Based on joint works with Benny Applebaum, Guy Kindler, David Steurer,
Discrete Gaussian Leftover Hash Lemma Shweta Agrawal IIT Delhi With Craig Gentry, Shai Halevi, Amit Sahai.
11 Provable Security. 22 Given a ciphertext, find the corresponding plaintext.
The Learning With Errors Problem Oded Regev Tel Aviv University (for more details, see the survey paper in the proceedings) Cambridge, 2010/6/11.
Lattice-Based Cryptography. Cryptographic Hardness Assumptions Factoring is hard Discrete Log Problem is hard  Diffie-Hellman problem is hard  Decisional.
CMSC 414 Computer and Network Security Lecture 6 Jonathan Katz.
Asymmetric Cryptography part 1 & 2 Haya Shulman Many thanks to Amir Herzberg who donated some of the slides from
CMSC 414 Computer and Network Security Lecture 9 Jonathan Katz.
Oded Regev Tel-Aviv University On Lattices, Learning with Errors, Learning with Errors, Random Linear Codes, Random Linear Codes, and Cryptography and.
CMSC 414 Computer and Network Security Lecture 6 Jonathan Katz.
Lattice-Based Cryptography
Fall 2010/Lecture 311 CS 426 (Fall 2010) Public Key Encryption and Digital Signatures.
15-853Page :Algorithms in the Real World Error Correcting Codes I – Overview – Hamming Codes – Linear Codes.
Cramer-Shoup is Plaintext Aware in the Standard Model Alexander W. Dent Information Security Group Royal Holloway, University of London.
CSC2535: 2013 Advanced Machine Learning Lecture 3a: The Origin of Variational Bayes Geoffrey Hinton.
Foundations of Cryptography Rahul Jain CS6209, Jan – April 2011
CMSC 414 Computer and Network Security Lecture 3 Jonathan Katz.
Correlation testing for affine invariant properties on Shachar Lovett Institute for Advanced Study Joint with Hamed Hatami (McGill)
A Secure Protocol for Computing Dot-products in Clustered and Distributed Environments Ioannis Ioannidis, Ananth Grama and Mikhail Atallah Purdue University.
Template attacks Suresh Chari, Josyula R. Rao, Pankaj Rohatgi IBM Research.
Lattice-Based Cryptography: From Practice to Theory to Practice Vadim Lyubashevsky INRIA / CNRS / ENS Paris (September 12, 2011)
Public Key Cryptography. symmetric key crypto requires sender, receiver know shared secret key Q: how to agree on key in first place (particularly if.
Search to Decision Reductions for Knapsacks and LWE 1 October 3, 2011 Daniele Micciancio, Petros Mol UCSD Theory Seminar.
MIMO continued and Error Correction Code. 2 by 2 MIMO Now consider we have two transmitting antennas and two receiving antennas. A simple scheme called.
Fast algorithm for the Shortest Vector Problem er (joint with Aggarwal, Dadush, and Stephens-Davidowitz) Oded Regev Courant Institute, NYU UC Irvine, Sloan.
DIGITAL COMMUNICATIONS Linear Block Codes
Digital Communications I: Modulation and Coding Course Term Catharina Logothetis Lecture 9.
Lattice-based cryptography and quantum Oded Regev Tel-Aviv University.
Copyright 2012, Toshiba Corporation. A Survey on the Algebraic Surface Cryptosystems Koichiro Akiyama ( TOSHIBA Corporation ) Joint work with Prof. Yasuhiro.
Randomness Extraction Beyond the Classical World Kai-Min Chung Academia Sinica, Taiwan 1 Based on joint works with Xin Li, Yaoyun Shi, and Xiaodi Wu.
CS519, © A.SelcukDifferential & Linear Cryptanalysis1 CS 519 Cryptography and Network Security Instructor: Ali Aydin Selcuk.
Pseudo-random generators Talk for Amnon ’ s seminar.
Error-Correcting Codes and Pseudorandom Projections Luca Trevisan U.C. Berkeley.
Comparing Notions of Full Derandomization Lance Fortnow NEC Research Institute With thanks to Dieter van Melkebeek.
Does Privacy Require True Randomness? Yevgeniy Dodis New York University Joint work with Carl Bosley.
An additive combinatorics approach to the log-rank conjecture in communication complexity Noga Zewi Technion – Israel Institute of Technology Joint work.
Richard Cleve DC 2117 Introduction to Quantum Information Processing QIC 710 / CS 667 / PH 767 / CO 681 / AM 871 Lecture (2011)
Additive Combinatorics in Theoretical Computer Science Shachar Lovett (UCSD)
@Yuan Xue CS 285 Network Security Block Cipher Principle Fall 2012 Yuan Xue.
Non-malleable Reductions and Applications Divesh Aggarwal * Yevgeniy Dodis * Tomasz Kazana ** Maciej Obremski ** Non-Malleable Codes from Two-Source Extractors.
Key Exchange in Systems VPN usually has two phases –Handshake protocol: key exchange between parties sets symmetric keys –Traffic protocol: communication.
Complexity Theory and Explicit Constructions of Ramsey Graphs Rahul Santhanam University of Edinburgh.
Tali Kaufman (Bar-Ilan)
Algebraic Property Testing:
Computational Fuzzy Extractors
Modern symmetric-key Encryption
CS154, Lecture 18:.
The Learning With Errors Problem
Cryptography Lecture 12.
Background: Lattices and the Learning-with-Errors problem
Cryptography Lecture 5.
Cryptography Lecture 19.
Cryptography Lecture 6.
Linear sketching with parities
Linear sketching over
Linear sketching with parities
Dan Boneh, Yuval Ishai, Alain Passelègue, Amit Sahai, and David J. Wu
On The Quantitative Hardness of the Closest Vector Problem
Cryptography Lecture 5.
Cryptography Lecture 11.
Cryptography Lecture 24.
Presentation transcript:

On Public Key Encryption from Noisy Codewords Yuval Ishai Technion & UCLA Eli Ben-Sasson (Technion) Iddo Ben-Tov (Technion) Ivan Damgård (Aarhus) Noga Ron-Zewi (IAS & DIMACS)

The Big Picture Theory of cryptography has many open questions –Feasibility: Do “secure multilinear maps” exist? –Efficiency: 2 t -secure PKE with O(t)-bit ciphertexts? Typical methodology: Prove result X under “acceptable” assumption Y –Notion of “acceptable” is somewhat arbitrary –What if this methodology fails? Alternative methodology (this talk): –Identify a class C of natural constructions –Identify a class A of natural attacks –Study existence and efficiency of constructions from C resisting A A combinatorial problem, no inherent barriers Systematic way for navigating “crypto dark matter” May lead to new acceptable assumptions

The Big Picture Provable consequences of acceptable assumptions Natural attacks

The Big Picture Heuristic constructions resisting natural attacks Natural attacks

Public Key Encryption Easy to achieve heuristically, many solid candidates Private key encryption:  Relatively few candidates  Non-trivial attacks (e.g., sub-exponential attacks, quantum attacks)  Less efficient in practice (e.g., RSA vs. AES) Public key encryption:

Are current PKE candidates asymptotically optimal? Consider goal of encrypting 1-bit message with security against circuits of size 2 t –Allow small probability of decryption error Optimization questions: –Minimize public key and ciphertext length –Minimize decryption time (circuit size) –Ideally: O(t) Current candidates fall short of ideal –Factoring –Discrete logarithm –Error-correcting codes (McEliece/Niederreiter-style) –Error-correcting codes / lattices (Alekhnovich/Regev-style)

PKE from Noisy Codewords Three well-known PKE schemes: –Alekhnovich (Alek) [FOCS 2003] –Regev [STOC 2005] –Gentry-Peikert-Vaikuntanathan (GPV) [STOC 2008] Common syntactic features: –Public random linear code C: n-dim subspace of F q m –Public key and ciphertext of the form w+e w is a secret random codeword in C (or a related public C’) e is a secret noise vector (Alternatively: syndrome H(w+e)=He) –Decryption via inner product in F q m Security relies on pseudorandomness of w+e –Follows from “low-noise LPN” (Alek) or LWE (Regev,GPV)

Regev, GPV q= field size = poly(m) Noise = small discrete Gaussian Alek Binary field (q=2) Noise = Binary field: concrete efficiency [Hopper-Blum01, Damgård-Park12, Pietrzak12, …]  Large field size  Brute-force -attack: Security against -attacks => public keys / ciphertexts of size  Lattice-based -attack: Security against -attacks => public keys / ciphertexts of size = max(|pk|,|ct|) PKE from Noisy Codewords

Alek  -attack Binary field Regev, GPV  -attack  Large field Dream goal Binary field -secure Second-best Binary field  -secure Main question: Noise distributions over binary field with better security guarantees? PKE from Noisy Codewords

Our Results Unified framework for PKE from noisy codewords –Captures Alek, Regev, GPV, extends [Micciancio10] –Allows arbitrary choice of field size and noise distributions Unconditionally ruling out dream goal –2 O(m/logm) attack for any distribution over the binary field –Based on agnostic learning of parities [Blum-Kalai-Wasserman03, Kalai-Verbin-Mansour08] –Implies LPN algorithm with n 1+ε samples in time 2 O(n/loglogn) [Lyubashevsky05, Kopparty-Saraf10]

Our Results Main result: Connecting “second best” to additive combinatorics – -time attack for any distribution over the binary field, assuming approximate duality conjecture –Flip side: counter-examples to conjecture likely to yield PKE candidates with useful features Study possibility of perfect decryption over constant- size rings –Negative result over F 2 –Candidate construction (?) over constant-size rings using “matching vector” families

The Unified Framework Parameters: Field, noise distributions over such that efficiently distinguishable Key Generation [Noise] [Code] [Noisy codeword] Encryption (of bit b) Decryption E.g., μ sk =(μ,1) μ b =(μ’,b), |μ|,|μ’| < m 1/2

Unified Framework Claim: For each {Alek,Regev,GPV} there exists a choice of parameters such that the unified scheme is equivalent to in terms of security. Alek q=2, noise distribution = Regev, GPV q=poly(m), noise distribution = small discrete Gaussian w + e random codeword in C noise vector Noisy Codeword H  (w+e) = H  e Parity-check matrix for C Syndrome

How to Pick Noise? Focus on q=2 Simplified question: find μ,μ’~ F 2 m such that: – is strongly biased towards 0 –LPN is hard with respect to both μ and μ’ Natural approaches –μ,μ’ of weight < m 1/2  low entropy  brute force attack –μ,μ’ in dual linear spaces V,V’  linear algebra attack –Combinations of above  combinations of attacks Can we add entropy while avoiding linear structure? –Related to hard questions in additive combinatorics –Polynomial Freiman-Ruzsa (PFR) conjecture: |A+A|≤k|A|  k -c -dense subset A’ with |span(A’)|≤k c |A|

Duality measure: Approximate Duality

Duality measure: Thm. [BenSasson-RonZewi11] Approximate Duality

Approximate Duality Conjecture Thm. [BenSasson-Lovett-RonZewi12] Assuming PFR conjecture, Approximate duality conjecture: Tight for A=B=all vectors of hamming weight. Implied by stronger PFR-type conjectures Applications in complexity theory: Construction of two-source extractors [BenSasson-RonZewi11] Relating rank to communication complexity [BLR12] Lower bounds on matching vector codes [Bhowmick-Dvir-Lovett13] Provable variant over reals [Lovett14]

-attack over Binary Field Either μ or μ’ have a dense core which is covered by “few” low-dimensional affine spaces –Attack either pk or ct via brute force + linear algebra Technicalities –Reduce “weighted” version of approximate duality conjecture to unweighed version –Deal with false positives

-secure noise distributions over binary field? Approximate duality conjecture True Applications in complexity theory Strong candidate PKE False Open Questions

Non-binary fields –Conjecture makes sense for constant-size fields –For what field size does attack break down? –How about constant-size rings? Uniform attacks? –Our attacks are inherently nonuniform –Uniform version of approximate duality? Eliminating decryption error –Impossible over F 2. How about F 5 ? Z 6 ?