Tight Bounds For Hashing Block Sources

Chung Kai-min, Vadhan Salil. Arxiv 2008

[Paper]    
ARXIV Independent

It is known that if a 2-universal hash function $H$ is applied to elements of a {\em block source} $(X_1,\dots ,X_T)$, where each item $X_i$ has enough min-entropy conditioned on the previous items, then the output distribution $(H,H(X_1),\dots ,H(X_T))$ will be ``close’’ to the uniform distribution. We provide improved bounds on how much min-entropy per item is required for this to hold, both when we ask that the output be close to uniform in statistical distance and when we only ask that it be statistically close to a distribution with small collision probability. In both cases, we reduce the dependence of the min-entropy on the number $T$ of items from $2logT$ in previous work to $logT$, which we show to be optimal. This leads to corresponding improvements to the recent results of Mitzenmacher and Vadhan (SODA `08) on the analysis of hashing-based algorithms and data structures when the data items come from a block source.

Similar Work

• Content-aware Neural Hashing For Cold-start Recommendation
• Content-aware Neural Hashing For Cold-start Recommendation
• Fast Locality-sensitive Hashing Frameworks For Approximate Near Neighbor Search
• Optimal Lower Bounds For Locality Sensitive Hashing (except When Q Is Tiny)