Balls into Bins Made Faster

Khosla, Megha

doi:10.1007/978-3-642-40450-4_51

Cited by 14 publications

(28 citation statements)

References 15 publications

(23 reference statements)

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“…We give a construction that uses Cuckoo hashing [66] to allocate balls to bins. However, the same method can be used with other algorithms (e.g., multi-choice Greedy [13], LocalSearch [55]) to obtain different parameters. We give a brief summary of Cuckoo hashing's allocation algorithm below.…”

Section: A Pbc From Reverse Cuckoo Hashingmentioning

confidence: 99%

PIR with Compressed Queries and Amortized Query Processing

Angel

Chen

Laine

et al. 2018

2018 IEEE Symposium on Security and Privacy (SP)

112

126

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Private information retrieval (PIR) is a key building block in many privacy-preserving systems. Unfortunately, existing constructions remain very expensive. This paper introduces two techniques that make the computational variant of PIR (CPIR) more efficient in practice. The first technique targets a recent class of CPU-efficient CPIR protocols where the query sent by the client contains a number of ciphertexts proportional to the size of the database. We show how to compresses this query, achieving size reductions of up to 274×.The second technique is a new data encoding called probabilistic batch codes (PBCs). We use PBCs to build a multi-query PIR scheme that allows the server to amortize its computational cost when processing a batch of requests from the same client. This technique achieves up to 40× speedup over processing queries one at a time, and is significantly more efficient than related encodings. We apply our techniques to the Pung private communication system, which relies on a custom multi-query CPIR protocol for its privacy guarantees. By porting our techniques to Pung, we find that we can simultaneously reduce network costs by 36× and increase throughput by 3×.

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Section: A Pbc From Reverse Cuckoo Hashingmentioning

confidence: 99%

PIR with Compressed Queries and Amortized Query Processing

Angel

Chen

Laine

et al. 2018

2018 IEEE Symposium on Security and Privacy (SP)

112

126

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“…Moreover, we restrict ourselves to its application to finding minimum weight perfect matchings in bipartite graphs. Our analysis technique is inspired by the LSA method [23] which is used to construct large hash tables and finding maximum matchings in unweighted bipartite graphs. In fact, the original motivation was to extend the label based technique in LSA for solving the weighted version of the matching problem in bipartite graphs.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Auction Algorithm for Weighted Bipartite Perfect Matchings

Khosla,

Anand

2021

Preprint

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We study the classical weighted perfect matchings problem for bipartite graphs or sometimes referred to as the assignment problem, i.e., given a weighted bipartite graph G = (U ∪ V, E) with weights w : E → R we are interested to find the maximum matching in G with the minimum/maximum weight. In this work we present a new and arguably simpler analysis of one of the earliest techniques developed for solving the assignment problem, namely the auction algorithm. Using our analysis technique we present tighter and improved bounds on the runtime complexity for finding an approximate minumum weight perfect matching in k-left regular sparse bipartite graphs.

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“…The paper studied the efficiency of insertion via Breadth First Search and also reported on some experiments with the random walk approach. The paper by Khosla describes a nice linear time algorithm for placing the n items. The papers considered insertion by random walk and proved that the expected time to complete a round can be bounded by

\log^{2 + o_{d} false(1 false)} n

, where o d (1) tends to zero as d → ∞ .…”

Section: Introductionmentioning

confidence: 99%

On the insertion time of random walk cuckoo hashing

Frieze

Johansson

2018

Random Struct Algorithms

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Cuckoo Hashing is a hashing scheme invented by pervious study of Pagh and Rodler. It uses d ≥ 2 distinct hash functions to insert n items into the hash table of size m = (1 + ε)n. In their original paper they treated d = 2 and m = (2 + ε)n. It has been an open question for some time as to the expected time for Random Walk Insertion to add items when d > 2. We show that if the number of hash functions d ≥ dε = O(1) then the expected insertion time is O(1) per item.

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Balls into Bins Made Faster

Cited by 14 publications

References 15 publications

PIR with Compressed Queries and Amortized Query Processing

PIR with Compressed Queries and Amortized Query Processing

Revisiting the Auction Algorithm for Weighted Bipartite Perfect Matchings

On the insertion time of random walk cuckoo hashing

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