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Deterministic extractors for bit-fixing sources by obtaining an independent seed

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  1. Seedless Deterministic extractors for bit-fixing sources by obtaining an independent seed Ariel Gabizon Ran Raz Ronen Shaltiel

  2. Randomness extractors (motivation) Randomness is essential in Computer Science: • Cryptography (!!) • Distributed Protocols (!) • Probabilistic Algorithms (?) Algorithm designers always assume that we have access to a stream of independent unbiassed coin tosses. How can we obtain random bits?

  3. We have access to distributions in nature: Weather (?) Particle reactions Key strokes of user Timing of past events These distributions are “somewhat random” but not “truly random”. Solution: Randomness Extractors Randomness Extractor Refining randomness from nature Somewhat random random coins Probabilistic algorithm input output

  4. C is a class of distributions over n bit strings. A deterministic (seedless) C-extractor is a function E such that for every XєC, E(X) is ε-close to uniform. A seededC-extractor has an additional (short i.e. log n) independent random seed as input. Extractor seed random output Randomness Extractors: Definition and two flavors source distribution from C Deterministic Seeded Two distributions are ε-closeif the probability they assign to any event differs by at most ε.

  5. Deterministic von-Neumann sources [vN51]. Markov Chains [Blu84]. Several independent sources [SV86,V86,V87,VV88,CG88,DEOR04,BIW04]. Samplable sources [TV00]. Seeded High min-entropy distributions [Z91,NZ93]. Lower bound of log n on the seed length [NZ93,RT99]. Explicit constructions coming close to matching bound (mass of work). A brief survey of randomness extractors Extractors turn out to have lots of applications in TCS.

  6. Bit-fixing sources [CGHFRS85] • An (n,k)-(oblivious) bit-fixing source is a distribution on n bit strings s.t. • k bits are uniformly distributed (good bits). • remaining n-k bits are fixed to arbitrary values (bad bits). k random bits

  7. Bit-fixing source extractors • The exclusive or function extracts one perfectly random bit. • Impossible to extract two perfect bits for k<n/3 [CGHFRS85]. • A probablistic argument gives an extractor which extracts k-O(log(n/ε)) bits (for statistical distance ε from uniform). • Best explicit construction extracts Ω(k2/n) bits [KZ03].

  8. Our results: We extract (1-o(1))k bits even for small k.

  9. Our approach • Start with an extractor that extracts few bits. • Convert into an extractor that extracts many bits.

  10. correlated! Getting more mileage from extractors: first attempt k random bits Deterministic Extractor Seeded Extractor Seeded Extractors are only guaranteed to work when the source and seed are independent. random output

  11. Solution: Seed obtainers k random bits X Seed Obtainer We require that X’ and Y are independent! We obtain a seed! bit fixing source random output X’ Y

  12. A seed obtainer is a function F(X)=(X’,Y) s.t. For every (n,k)-bit-fixing source X: X’ is an (n’,k’)-bit-fixing source with (n’,k’)≈(n,k). Y is uniformly distributed. X’ and Y are independent. Seeded Extractor Seed Obtainer random output Seed obtainer: Definition X F(X) is close to a convex combination of distributions X’,Y s.t. X’ Y Seed obtainers allow us to get more randomness from deterministic bit-fixing source extractors.

  13. Construction of seed obtainers (erasing the correlation) We will pretend red bits are fixed! The extractor won’t know! • For any set (and in particular set of good bits) The sampled set hits it in the “correct” proportion. • Set parameters so that: • few red bits are in. • Most red bits are out. k random bits X Warning: Intuition is oversimplified! Intuition: Erase parts that are correlated with Y Seed obtainer Deterministic Extractor correlated! W bit fixing source seed for averaging sampler Y X’ random output

  14. We use the [KZ03] deterministic extractor as basis for the seed-obtainer. Attach a good seeded extractor [RRV99]. Seeded Extractor Seed Obtainer random output Construction for k>n½ X X’ Y

  15. The case of k<n½ • We need a deterministic bit-fixing source extractor to start with. • The tecnique of [KZ03] also works for k<n½, but extracts very few bits. • Only Ω(log k) bits. • For k=polylog n, we get only log log n bits. • Not sufficient for seeded extractors! • (Also not sufficient for standard averaging samplers.)

  16. We construct a seeded bit-fixing source extractor that uses seed O(log log n) and extract (1-o(1))k bits. Apply it after the seed obtainer. Seededbit-fixing Extractor Seed Obtainer random output Solution: seeded bit-fixing source extractor. X X’ Y

  17. We partition the source into about log n blocks. Each bit tosses a coin to decide on its block. We use ε-pairwise dependent coins [NN93]. Cost: O(log log n) random bits. w.h.p. each block contains at least one good bit. Each block outputs the xor of its bits. log n A Seeded extractor for bit-fixing sources: log log n -> log n Output log n random bits.

  18. We have O(log log n) random bits as seed. Use O(log log n) random bits to partition into two blocks. Use seeded bit-fixing extractor from previous slide to extract log n bits. Use the output as a seed for a (standard) seeded extractor. To extract (1-o(1))k bits. n/log n A Seeded extractor for bit-fixing sources: log n -> (1-o(1))k prvs Seeded extractor log n bits

  19. Note on averaging samplers • Ingredient in the seed obtainer construction. • We need to sample subsets of {1..n}. • Sampling one element: log n bits. • We already saw: Sampling based on ε-pairwise dependence: log log n bits [EGLNV95,RSW00]. • ?????? • Possible because query complexity is huge (n/log n). • Note: We need samplers that hit very small sets (size<n½)) and cannot use samplers based on (seeded) extractors.

  20. We construct deterministic bit-fixing extractors that: Extract almost all randomnes. Work even for small k. Introduce “seed obtainers”. Allow getting more random bits from deterministc bit-fixing extractors. Construction for small k uses seeded bit-fixing extractor, that uses seed of length O(log log n) to “partition” source. Seeded Extractor Seed Obtainer random output Overview X X’ Y

  21. Open problems • Improve error for small k (say k<n½). • Possible direction: Construct deterministic bit-fixing source with larger output (>>log k) for small k. • Can this technique be applied to seeded extractors? (probably not).

  22. That’s it