TY - GEN
T1 - BFLUT Bloom Filter for Private Look Up Tables
AU - Dolev, Shlomi
AU - Gudes, Ehud
AU - Segev, Erez
AU - Ullman, Jeffrey D.
AU - Weintraub, Grisha
N1 - Publisher Copyright: © 2022, Springer Nature Switzerland AG.
PY - 2022/6/23
Y1 - 2022/6/23
N2 - Open addressing hash tables, possibly under double hashing policy, are regarded more memory efficient than linked list hashing; as the memory used for pointers can be used for a longer table, and allow better-expected performance as the load factor is smaller and there are fewer expected collisions. We suggest further eliminating the single pointer to the memory location used in each entry of the open addressing, and using a single bit per entry, namely use a Bloom Filter to encode the memory address. Thus, obtain even a better load factor, with the same memory, and less number of wrongly mapped addresses when the load is low. Moreover, we can prove that the content in the lookup table that is based on the bloom filter is pseudo-random (in the level of randomization implied by the hash function), thus, keeping the items and the addresses that the LookUp Table (LUT) encodes private.
AB - Open addressing hash tables, possibly under double hashing policy, are regarded more memory efficient than linked list hashing; as the memory used for pointers can be used for a longer table, and allow better-expected performance as the load factor is smaller and there are fewer expected collisions. We suggest further eliminating the single pointer to the memory location used in each entry of the open addressing, and using a single bit per entry, namely use a Bloom Filter to encode the memory address. Thus, obtain even a better load factor, with the same memory, and less number of wrongly mapped addresses when the load is low. Moreover, we can prove that the content in the lookup table that is based on the bloom filter is pseudo-random (in the level of randomization implied by the hash function), thus, keeping the items and the addresses that the LookUp Table (LUT) encodes private.
UR - http://www.scopus.com/inward/record.url?scp=85134148551&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-07689-3_35
DO - https://doi.org/10.1007/978-3-031-07689-3_35
M3 - Conference contribution
SN - 978-3-031-07688-6
VL - 13301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 499
EP - 505
BT - Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
A2 - Dolev, Shlomi
A2 - Meisels, Amnon
A2 - Katz, Jonathan
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
Y2 - 30 June 2022 through 1 July 2022
ER -