Solving sparse linear equations over finite fields
IEEE Transactions on Information Theory
A tale of three spelling checkers
Software—Practice & Experience
OPUS: preventing weak password choices
Computers and Security
An algorithm for approximate membership checking with application to password security
Information Processing Letters
Summary cache: a scalable wide-area web cache sharing protocol
IEEE/ACM Transactions on Networking (TON)
Membership in Constant Time and Almost-Minimum Space
SIAM Journal on Computing
A second look at bloom filters
Communications of the ACM
Designing a Bloom filter for differential file access
Communications of the ACM
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
IEEE/ACM Transactions on Networking (TON)
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
The Bloomier filter: an efficient data structure for static support lookup tables
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
An optimal Bloom filter replacement
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Succinct Data Structures for Retrieval and Approximate Membership (Extended Abstract)
ICALP '08 Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I
Bloomier Filters: A Second Look
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
The context of coordinating groups in dynamic mobile networks
COORDINATION'11 Proceedings of the 13th international conference on Coordination models and languages
Approximate membership query over time-decaying windows for event stream processing
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Various improvements to text fingerprinting
Journal of Discrete Algorithms
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We suggest a method for holding a dictionary data structure, which maps keys to values, in the spirit of Bloom Filters. The space requirements of the dictionary we suggest are much smaller than those of a hashtable. We allow storing n keys, each mapped to value which is a string of k bits. Our suggested method requires nk + o (n ) bits space to store the dictionary, and O (n ) time to produce the data structure, and allows answering a membership query in O (1) memory probes. The dictionary size does not depend on the size of the keys . However, reducing the space requirements of the data structure comes at a certain cost. Our dictionary has a small probability of a one sided error. When attempting to obtain the value for a key that is stored in the dictionary we always get the correct answer. However, when testing for membership of an element that is not stored in the dictionary, we may get an incorrect answer, and when requesting the value of such an element we may get a certain random value. Our method is based on solving equations in GF (2 k ) and using several hash functions. Another significant advantage of our suggested method is that we do not require using sophisticated hash functions. We only require pairwise independent hash functions. We also suggest a data structure that requires only nk bits space, has O (n 2) preprocessing time, and has a O (logn ) query time. However, this data structures requires a uniform hash functions. In order replace a Bloom Filter of n elements with an error proability of 2*** k , we require nk + o (n ) memory bits, O (1) query time, O (n ) preprocessing time, and only pairwise independent hash function. Even the most advanced previously known Bloom Filter would require nk + O (n ) space, and a uniform hash functions, so our method is significantly less space consuming especially when k is small. Our suggested dictionary can replace Bloom Filters, and has many applications. A few application examples are dictionaries for storing bad passwords, differential files in databases, Internet caching and distributed storage systems.