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2008 Proceedings IEEE INFOCOM - The 27th Conference on Computer Communications, 2008
A bloom filter is a randomized data structure for performing approximate membership queries. It is being increasingly used in networking applications ranging from security to routing in peer to peer networks. In order to meet a given false positive rate, the amount of memory required by a bloom filter is a function of the number of elements in the set.
Fang Hao +2 more
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A bloom filter is a randomized data structure for performing approximate membership queries. It is being increasingly used in networking applications ranging from security to routing in peer to peer networks. In order to meet a given false positive rate, the amount of memory required by a bloom filter is a function of the number of elements in the set.
Fang Hao +2 more
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Proceedings of the 2017 VI International Conference on Network, Communication and Computing, 2017
A standard technique from the cryptanalysis is to use exhaustive search that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the hash value. But this will take a lot of storage space and the time spent on query will be very long.
Yuanhang Yang, Shuhui Chen
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A standard technique from the cryptanalysis is to use exhaustive search that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the hash value. But this will take a lot of storage space and the time spent on query will be very long.
Yuanhang Yang, Shuhui Chen
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2019 International Conference on Electronics, Information, and Communication (ICEIC), 2019
A membership identification is a key functionality in many network applications. Various data structures have been introduced in order to support the efficient membership identification. Since a Bloom filter can provide simple but efficient membership checking, it is widely used in many network applications.
Ju Hyoung Mun, Hyesook Lim
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A membership identification is a key functionality in many network applications. Various data structures have been introduced in order to support the efficient membership identification. Since a Bloom filter can provide simple but efficient membership checking, it is widely used in many network applications.
Ju Hyoung Mun, Hyesook Lim
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Effect of Hadamard multiplication on bloom filter and double bloom filter transformations
SECURITY AND PRIVACY, 2023AbstractIris biometric is the most common and widely accepted biometric authentication method due to its high accuracy. The iris biometric template should be protected to overcome security and privacy attacks. The two standard iris biometric template protection methods are bloom filter and double bloom filter‐based feature transformations.
Ajish Sreedharan, K. S. Anil Kumar
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IEEE/ACM Transactions on Networking, 2001
A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. Although Bloom filters allow false positives, for many applications the space savings outweigh this draw-back when the probability of an error is sufficiently low.
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A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. Although Bloom filters allow false positives, for many applications the space savings outweigh this draw-back when the probability of an error is sufficiently low.
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2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC), 2016
A Bloom filter is a space-efficient probabilistic data structure that is used in many domains including networking applications to test for set memberships. Such applications often require sending Bloom filters using messages. Consequently, it is important to minimize the size of the filters such that the storage, transmission, and processing costs are
Negar Mosharraf +2 more
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A Bloom filter is a space-efficient probabilistic data structure that is used in many domains including networking applications to test for set memberships. Such applications often require sending Bloom filters using messages. Consequently, it is important to minimize the size of the filters such that the storage, transmission, and processing costs are
Negar Mosharraf +2 more
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Complement Bloom Filter for Identifying True Positiveness of a Bloom Filter
IEEE Communications Letters, 2015The use of Bloom filters in network applications has increased rapidly. Since Bloom filters can produce false positives, the trueness of each positive needs to be identified by referring to an off-chip hash table. This letter proposes a new method for identifying the trueness of Bloom filter positives.
Hyesook Lim, Jungwon Lee, Changhoon Yim
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A Bloom filter variant for Blockchain
Concurrency and Computation: Practice and Experience, 2021AbstractBlockchain is a single linked list of blocks consisting of transactions identified by their hash value. Querying Blockchain, primarily searching blocks/transactions, can be treated as membership queries and efficiently evaluated by a Bloom filter (BF).
Xing Fan, Baoning Niu
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2015
Modern databases tailored to highly distributed, fault tolerant management of information for big data applications exploit a classical data structure for reducing disk and network I/O as well as for managing data distribution: The Bloom filter. This data structure allows to encode small sets of elements, typically the keys in a key-value store, into a
Martin Werner 0001, Mirco Schönfeld
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Modern databases tailored to highly distributed, fault tolerant management of information for big data applications exploit a classical data structure for reducing disk and network I/O as well as for managing data distribution: The Bloom filter. This data structure allows to encode small sets of elements, typically the keys in a key-value store, into a
Martin Werner 0001, Mirco Schönfeld
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Ternary Bloom filter replacing counting Bloom filter
2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), 2016A counting Bloom filter (CBF) generalizes a standard 1-bit vector Bloom filter and allows not only membership queries but also insertion and deletion operations for dynamic sets. However, the CBF can cause false negatives because of counter overflows. A 4-bit vector CBF, which provides the probability of false negatives sufficiently small, is generally
Hayoung Byun, Jungwon Lee, Hyesook Lim
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