Skip to main content

Deterministic Extractors for Bit-Fixing Sources by Obtaining an Independent Seed

  • Chapter
  • First Online:
Deterministic Extraction from Weak Random Sources
  • 507 Accesses

Summary

An \((n,k)\)-bit-fixing source is a distribution X over \({\{0,1\}}^n\) such that there is a subset of k variables in \(X_1,\ldots,X_n\) which are uniformly distributed and independent of each other, and the remaining \(n-k\) variables are fixed. A deterministic bit-fixing source extractor is a function \(E:{\{0,1\}}^n {\rightarrow} {\{0,1\}}^m\) which on an arbitrary \((n,k)\)-bit-fixing source outputs m bits that are statistically-close to uniform. Prior to our work, Kamp and Zuckerman [44th FOCS, 2003] gave a construction of a deterministic bit-fixing source extractor that extracts \(\Omega(k^2/n)\) bits and requires \(k>\sqrt{n}\).

In this chapter we give constructions of deterministic bit-fixing source extractors that extract \((1-o(1))k\) bits whenever \(k>(\log n)^c\) for some universal constant \(c>0\). Thus, our constructions extract almost all the randomness from bit-fixing sources and work even when k is small. For \(k \gg \sqrt{n}\) the extracted bits have statistical distance \(2^{-n^{\Omega(1)}}\) from uniform, and for \(k \le \sqrt{n}\) the extracted bits have statistical distance \(k^{-\Omega(1)}\) from uniform.

Our technique gives a general method to transform deterministic bit-fixing source extractors that extract few bits into extractors which extract almost all the bits. This work is the first to use the ‘recycling paradigm’ as described in the introduction. The description of it here is different and perhaps more cumbersome, as the one given in the introduction was only realized in hindsight.

This chapter is based on [26].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We remark that such sources are often referred to as “oblivious bit-fixing sources” to differentiate them from other types of “non-oblivious” bit-fixing sources in which the bits outside of S may depend on the bits in S (cf. [7]). In this chapter we are only concerned with the “oblivious case”.

  2. 2.

    This was observed independently by Lipton and Vishnoi [40].

  3. 3.

    In fact, a similar idea is used in [37] in order to reduce the case of large d to the case of \(d=2\).

  4. 4.

    We remark that some of the “standard techniques” for constructing averaging samplers (such as taking a walk on an expander graph or using a randomness extractor) perform poorly in this setup, and do not work when \(k < \sqrt{n}\) (even if T is allowed to be a multi-set). This happens because in order to even hit a set S of size k, these techniques require sampling a (multi-)set T of size larger than \((n/k)^2\), which is larger than n for \(k<\sqrt{n}\). In contrast, note that a completely random set of size roughly \(n/k\) will hit a fixed set S of small size with high probability.

Bibliography

  1. N. Alon, O. Goldreich, J. Håstad, and R. Peralta. Simple constructions of almost k-wise independent random variables. In Proceedings of the 31st Annual IEEE Symposium on Foundations of Computer Science, volume II, pages 544–553, 1990.

    Google Scholar 

  2. B. Barak, G. Kindler, R. Shaltiel, B. Sudakov, and A. Wigderson. Simulating independence: New constructions of condensers, Ramsay graphs, dispersers, and extractors. In Proceedings of the 37th Annual ACM Symposium on Theory of Computing, pages 1–10, 2005.

    Google Scholar 

  3. M. Bellare and J. Rompel. Randomness-efficient oblivious sampling. FOCS 1994.

    Google Scholar 

  4. M. Ben-Or and N. Linial. Collective coin flipping. ADVCR: Advances in Computing Research, 5:91–115, 1989.

    Google Scholar 

  5. V. Boyko. On the security properties of OAEP as an all-or-nothing transform. In Proc. 19th International Advances in Cryptology Conference – CRYPTO ’99, pages 503–518, 1999.

    Google Scholar 

  6. R. Canetti, Y. Dodis, S. Halevi, E. Kushilevitz, and A. Sahai. Exposure-resilient functions and all-or-nothing transforms.Lecture Notes in Computer Science, 1807, 2000.

    Google Scholar 

  7. B. Chor, O. Goldreich, J. Hastad, J. Friedman, S. Rudich, and R. Smolensky. The bit extraction problem or t-resilient functions. InProceedings of the 26th Annual IEEE Symposium on Foundations of Computer Science, pages 396–407, 1985.

    Google Scholar 

  8. Y. Dodis.Exposure-Resilient Cryptography. PhD thesis, Department of Electrical Engineering and Computer Science, MIT, August 2000.

    Google Scholar 

  9. Y. Dodis, A. Sahai, and A. Smith. On perfect and adaptive security in exposure-resilient cryptography.Lecture Notes in Computer Science, 2045, 2001.

    Google Scholar 

  10. S. Even, O. Goldreich, M. Luby, N. Nisan, and B. Velickovic. Efficient approximation of product distributions.RSA: Random Structures & Algorithms, 13, 1998.

    Google Scholar 

  11. A.Gabizon, R. Raz, and R. Shaltiel. Deterministic extractors for bit-fixing sources by obtaining an independent seed.SICOMP: SIAM Journal on Computing, 36(4):1072–1094, 2006.

    Article  MathSciNet  MATH  Google Scholar 

  12. O. Goldreich. A sample of samplers – A computational perspective on sampling (survey). InECCCTR: Electronic Colloquium on Computational Complexity, technical reports, 1997a.

    Google Scholar 

  13. J. Kamp and D. Zuckerman. Deterministic extractors for bit-fixing sources and exposure-resilient cryptography.SIAM J. Comput, 36(5):1231–1247, 2007.

    Article  MathSciNet  Google Scholar 

  14. R. Lipton and N. Vishnoi. Manuscript. 2004.

    Google Scholar 

  15. L. Lovasz.Combinatorial Problems and Exercises. North-Holland, Amsterdam, 1979.

    Google Scholar 

  16. J. Naor and M. Naor. Small-bias probability spaces: Efficient constructions and applications. In Proceedings of the 22nd Annual ACM Symposium on Theory of Computing, pages 213–223, 1990.

    Google Scholar 

  17. N. Nisan and D. Zuckerman. Randomness is linear in space. Journal of Computer and System Sciences, 52(1):43–52, 1996.

    Article  MathSciNet  MATH  Google Scholar 

  18. J. Radhakrishnan and A. Ta-Shma. Bounds for dispersers, extractors, and depth-two superconcentrators. SIAM Journal on Discrete Mathematics, 13(1):2–24, 2000.

    Article  MathSciNet  MATH  Google Scholar 

  19. R. Raz, O. Reingold, and S. Vadhan. Extracting all the randomness and reducing the error in Trevisan’s extractors. In Proceedings of the 31st Annual ACM Symposium on Theory of Computing, pages 149–158, 1999.

    Google Scholar 

  20. O. Reingold, R. Shaltiel, and A. Wigderson. Extracting randomness via repeated condensing. In Proceedings of the 41st Annual IEEE Symposium on Foundations of Computer Science, 2000.

    Google Scholar 

  21. R. Rivest. All-or-nothing encryption and the package transform. In Fast Software Encryption: 4th International Workshop, FSE, volume 1267 of Lecture Notes in Computer Science, 1997.

    Google Scholar 

  22. R. Shaltiel. Recent developments in explicit constructions of extractors. Bulletin of the EATCS, 77:67–95, 2002.

    MathSciNet  MATH  Google Scholar 

  23. S. Vadhan. On constructing locally computable extractors and cryptosystems in the bounded storage model, November 01 2002.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ariel Gabizon .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Gabizon, A. (2011). Deterministic Extractors for Bit-Fixing Sources by Obtaining an Independent Seed. In: Deterministic Extraction from Weak Random Sources. Monographs in Theoretical Computer Science. An EATCS Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14903-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14903-0_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14902-3

  • Online ISBN: 978-3-642-14903-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics