Joseph Naor and Moni Naor. Small-bias probability spaces: Efficient constructions and applications. In Proc. 22nd Annual ACM Symposium on Theory of Computing, pages 213--223, Baltimore, 1990. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
First 50 documents Next 50
Universal Service-Providers for Database Private.. - Di-Crescenzo, Ishai, .. (1999) (Correct)
....can be applied to the schemes from [20, 27] 26 GF(q) implying that at least n data strings must be tested. However, settling for a small probability of one sided error, a much more efficient solution to this problem can be obtained as a typical application of small bias probability spaces [22]. The following fact is proved in [22] Fact 2. For any n 2 N and ffl 0, there is (an efficiently constructible) meta test set T n;ffl (GF(q) where l = O(log ) such that: ffl jT n;ffl j is polynomial in n and 1=ffl. ffl For every y 2 GF(q) y 6= 0, at most an ffl fraction of ....
....27] 26 GF(q) implying that at least n data strings must be tested. However, settling for a small probability of one sided error, a much more efficient solution to this problem can be obtained as a typical application of small bias probability spaces [22] The following fact is proved in [22]. Fact 2. For any n 2 N and ffl 0, there is (an efficiently constructible) meta test set T n;ffl (GF(q) where l = O(log ) such that: ffl jT n;ffl j is polynomial in n and 1=ffl. ffl For every y 2 GF(q) y 6= 0, at most an ffl fraction of the test tuples (w 1 ; w l ) 2 ....
[Article contains additional citation context not shown here]
J. Naor and M. Naor. Small-bias probability spaces: Efficient constructions and applications. In Proc. of 22th STOC, pages 213--223, 1990.
Bounded Arithmetic and Lower Bounds in Boolean Complexity - Razborov (1993) (17 citations) (Correct)
....a non zero row vector fi of length L such that fiff = 0. Now, recall from Section 1.1 that U 1 (ffi) is exactly the theory capturing NC computations. Examples of fis needed in Facts 1, 2 are known to be NC computable from ff. For Fact 1 one could apply the standard derandomization procedure [16]; the NC algorithm for Fact 2 is based upon computing the matrix rank [15] Hence U 1 (ffi) can define relationals ffi witnessing Facts 1, 2 in the real world. It is not clear, however, to which extent U 1 (ffi) can prove the desired properties of these relationals. The point is that the ....
J. Naor and M. Naor. Small-bias probability spaces: Efficient constructions and applications. In Proceedings of the 22th ACM STOC, pages 213--223, 1990.
Randomized Swap Matching in O(m log m log |Sigma|) time - Cole, Hariharan (1999) (Correct)
....codes of length O(log j Sigmaj) each so that the following property holds for any ordered triple of codes: the first two codes in the triple are identical and different from the third code on at least one bit. Amir et al. 3] use the ffl biased k wise independent sample space of Naor and Naor [5] to obtain these codes in O(j Sigmaj) time. They use the following parameter values: k = 3 and ffl 1=8. Our contribution is two fold. First, we give a randomized algorithm for the case of alphabet sets of size 2. This algorithm produces the right answer with probability at least 1 Gamma m ....
J. Naor, M. Naor. Small Bias Probability Spaces: Efficient Constructions and Applications, SIAM J. Computing, 22, pp. 838--856, 1993.
Almost k-wise Independent Sample Spaces and Their.. - Kurosawa, Johansson.. (1997) (Correct)
....independent sample space is a probability space on m bit sequences such that any k bits are almost independent. A ffl biased sample space is a space in which any (boolean) linear combination of the m bits has the value 1 with probability close to 1=2. These notions were introduced by Naor and Naor [17] and further studied in [1] due to their applications to algorithms and complexity theory. However, there are also cryptographic applications: Krawczyk applied ffl biased sample spaces to the construction of authentication codes [13] In this paper, we investigate several new relationships between ....
J. Naor and M. Naor. Small bias probability spaces: efficient constructions and applications. SIAM Journal on Computing 22 (1993), 838--856.
Almost K-Wise Independence Versus K-Wise Independence - Alon, Goldriech, Mansour (Correct)
.... (i.e. every k bits are distributed almost uniformly) The reason being that the latter distributions can be generated using fewer random bits (i.e. O(k log(n=ffl) bits suffice, where ffl is the variation distance of these k projections to the uniform distribution) See the work of Naor and Naor [5] (as well as subsequent simplifications in [2] Note that, in both cases, replacing the algorithm s random tape by strings taken from a distribution of a smaller support requires verifying that the original analysis still holds for the replaced distribution. It would have been nicer, if instead ....
....unbiased over J . Then fi fi Pr[ Phi i2J Y i = 1] Gamma fi fi fi fi fi (1 Gamma p) Delta Pr[ Phi i2J X i = 1] p Delta Gamma fi fi = 1 Gamma p) Delta fi fi Pr[ Phi i2J X i = 1] Gamma fi fi The theorem follows. On one hand, we know (cf. 2] following [5]) that there exists ffl bias distributions of support size (n=ffl) On the other hand, we will show (in Lemma 3.1) that every k wise independent distribution, not only has large support (as proven, somewhat implicitly, in [6] and explicitly in [3] and [1] but also has a large min entropy ....
J. Naor and M. Naor. Small-bias Probability Spaces: Efficient Constructions and Applications. SIAM J. on Computing, Vol 22, 1993.
On the Construction of Pseudo-Random Permutations.. - Naor, Reingold (1996) (66 citations) Self-citation (Naor) (Correct)
No context found.
J. Naor and M. Naor, Small-bias probability spaces: efficient constructions and applications, SIAM J. Comput., vol. 22(4), 1993, pp. 838-856.
Splitters and Near-Optimal Derandomization - Naor, Schulman, Srinivasan (1995) (14 citations) Self-citation (Naor) (Correct)
No context found.
J. Naor and M. Naor, Small-bias probability spaces: efficient constructions and applications, SIAM J. on Computing 22, 1993, pp. 838--856.
Approximating Probability Distributions Using Small Sample.. - Azar, Motwani, Naor (1995) (1 citation) Self-citation (Naor) (Correct)
No context found.
J. Naor and M. Naor. Small-bias probability spaces: efficient constructions and applications. SIAM Journal on Computing, 22 (1993), pp. 838--856.
Preference Estimation and Query Learning - Blum, Jackson, al. (2004) (Correct)
No context found.
Joseph Naor and Moni Naor. Small-bias probability spaces: Efficient constructions and applications. In Proc. 22nd Annual ACM Symposium on Theory of Computing, pages 213--223, Baltimore, 1990.
Randomness versus Fault-Tolerance - Canetti, Kushilevitz, Ostrovsky.. (1999) (Correct)
No context found.
J. Naor, and M. Naor, "Small-Bias Probability Spaces: Efficient Constructions and Applications", STOC 90, and SIAM J. on Computing, Vol 22, No. 4, 1993, pp. 838--856.
Amortizing Randomness in Private Multiparty Computations - Kushilevitz, Ostrovsky.. (1998) (1 citation) (Correct)
No context found.
J. Naor, and M. Naor, "Small-Bias Probability Spaces: Efficient Constructions and Applications", STOC 90, and SIAM J. on Computing, Vol 22(4), pp. 838--856, 1993.
Amortizing Randomness in Private Multiparty Computations - Kushilevitz, Ostrovsky, Rosen (2002) (1 citation) (Correct)
No context found.
J. Naor, and M. Naor, "Small-Bias Probability Spaces: Efficient Constructions and Applications", STOC 90, and SIAM J. on Computing, Vol 22, No. 4, 1993, pp. 838--856.
A Randomness-Rounds Tradeoff in Private Computation - Kushilevitz, Rosen (8 citations) (Correct)
No context found.
J. Naor, and M. Naor, "Small-Bias Probability Spaces: Efficient Constructions and Applications ", STOC 90, and SIAM J. on Computing, Vol 22, No. 4, 1993, pp. 838--856.
On Spectral Properties of Graphs, and Their Application to.. - Bilu (2004) (Correct)
No context found.
J. Naor and M. Naor. Small-bias probability spaces: efficient constructions and applications. SIAM J. Comput., 22(4):838--856, 1993.
Small Pseudo-Random Families of Matrices: - Derandomizing Approximate Quantum (2004) (Correct)
No context found.
Joseph Naor, Moni Naor. Small-Bias Probability Spaces: Efficient Constructions and Applications. SIAM J. Comput. 22(4): 838-856 (1993).
Refining Randomness - Ta-Shma (1996) (Correct)
No context found.
Naor and Naor. Small-bias probability spaces: Efficient constructions and applications. SIAM Journal on Computing, 22, 1993.
Efficient Memory Integrity Verification and.. - Suh, Clarke.. (2003) (4 citations) (Correct)
No context found.
J. Naor and M. Naor. Small-bias probability spaces: efficient constructions and applications. In 22 nd ACM Symposium on Theory of Computing, pages 213--223, 1990.
Preference Elicitation and Query Learning - Blum, Jackson, Sandholm, Zinkevich (2003) (4 citations) (Correct)
No context found.
Naor, J., Naor, M.: Small-bias probability spaces: Efficient constructions and applications. In: Proc. 22nd Annual ACM Symposium on Theory of Computing, Baltimore (1990) 213--223
Extracting Randomness: A Survey and New Constructions - Nisan, Ta-Shma (1999) (32 citations) (Correct)
No context found.
Naor and Naor, Small-bias probability spaces: Efficient constructions and applications, SIAM J. Comput. 22 (1993).
Three Theorems Regarding Testing Graph Properties - Goldreich, Trevisan (2003) (7 citations) (Correct)
No context found.
J. Naor and M. Naor, Small-bias probability spaces: Efficient constructions and applications, SIAM J Comput 22 (1993), 838 -- 856.
Approximability of Probability Distributions - Alina Beygelzimer Beygel (2003) (Correct)
No context found.
J. Naor and M. Naor. Small-bias probability spaces: Efficient constructions and applications. In Proc. of the 22nd ACM Symposium on Theory of Computing (STOC), pages 213--223, 1990.
How Many Queries are Needed to Learn? - Lisa Hellerstein Eecs (1996) (2 citations) (Correct)
No context found.
J. Naor and M. Naor. Small-bias Probability Spaces: Efficient Constructions and Applications. Proceedings of the 22nd Annual ACM Symposium on Theory of Computing, pages 213--223, 1990.
Extractors from Reed-Muller Codes - Amnon Ta-Shma Department (Correct)
No context found.
J. Naor and M. Naor. Small--bias probability spaces: Efficient constructions and applications. SIAM Journal on Computing, 22(4):838--856, 1993.
Exposure-Resilient Functions and All-Or-Nothing.. - Canetti, Dodis, Halevi, .. (2000) (14 citations) (Correct)
No context found.
J. Naor, M. Naor. Small-Bias Probability Spaces: Efficient Constructions and Applications. In SIAM J. Computing, 22(4):838-856, 1993.
An Algorithmic Approach to the General Lovász Local.. - Czumaj, Scheideler (2000) (Correct)
No context found.
J. Naor and J. Naor. Small-bias probability spaces: Efficient constructions and applications. SIAM Journal on Computing, 22(4):838--856, 1993.
First 50 documents Next 50
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC