TY - JOUR
T1 - Randomness in private computations
AU - Kushilevitz, Eyal
AU - Mansour, Yishay
PY - 1997/11
Y1 - 1997/11
N2 - We consider the amount of randomness used in private distributed computations. Specifically, we show how n players can compute the exclusive-or (xor) of n boolean inputs t-privately, using only O(t2 log(n/t)) random bits (the best known upper bound is O(tn)). We accompany this result by a lower bound on the number of random bits required to carry out this task; we show that any protocol solving this problem requires at least t random bits (again, this significantly improves over the known lower bounds). For the upper bound, we show how, given m subsets of {1,...,n}, to construct in (deterministic) polynomial time a probability distribution of n random variables (i.e., a probability distribution over {0, }n) such that (1) the parity of random variables in each of these m subsets is 0 or 1 with equal probability, and (2) the support of the distribution is of size at most 2m. This construction generalizes previously considered types of sample spaces (such as κ-wise independent spaces and Schulman's spaces [Sample spaces uniform on neighborhoods, in Proc. of the 24th Annual ACM Symposium on Theory of Computing, ACM, New York, 1992, pp. 17-25]). We believe that this construction is of independent interest and may have various applications.
AB - We consider the amount of randomness used in private distributed computations. Specifically, we show how n players can compute the exclusive-or (xor) of n boolean inputs t-privately, using only O(t2 log(n/t)) random bits (the best known upper bound is O(tn)). We accompany this result by a lower bound on the number of random bits required to carry out this task; we show that any protocol solving this problem requires at least t random bits (again, this significantly improves over the known lower bounds). For the upper bound, we show how, given m subsets of {1,...,n}, to construct in (deterministic) polynomial time a probability distribution of n random variables (i.e., a probability distribution over {0, }n) such that (1) the parity of random variables in each of these m subsets is 0 or 1 with equal probability, and (2) the support of the distribution is of size at most 2m. This construction generalizes previously considered types of sample spaces (such as κ-wise independent spaces and Schulman's spaces [Sample spaces uniform on neighborhoods, in Proc. of the 24th Annual ACM Symposium on Theory of Computing, ACM, New York, 1992, pp. 17-25]). We believe that this construction is of independent interest and may have various applications.
KW - Privacy
KW - Randomness
KW - Small probability spaces
KW - Xor function
UR - http://www.scopus.com/inward/record.url?scp=0011180890&partnerID=8YFLogxK
U2 - 10.1137/S0895480196306130
DO - 10.1137/S0895480196306130
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AN - SCOPUS:0011180890
SN - 0895-4801
VL - 10
SP - 647
EP - 661
JO - SIAM Journal on Discrete Mathematics
JF - SIAM Journal on Discrete Mathematics
IS - 4
ER -