Efficient and Error-Correcting Data Structures for Membership and Polynomial Evaluation

Abstract : We construct efficient data structures that are resilient against a constant fraction of adversarial noise. Our model requires that the decoder answers most queries correctly with high probability and for the remaining queries, the decoder with high probability either answers correctly or declares "don't know." Furthermore, if there is no noise on the data structure, it answers all queries correctly with high probability. Our model is the common generalization of a model proposed recently by de Wolf and the notion of "relaxed locally decodable codes" developed in the PCP literature. We measure the efficiency of a data structure in terms of its length, measured by the number of bits in its representation, and query-answering time, measured by the number of bit-probes to the (possibly corrupted) representation. In this work, we study two data structure problems: membership and polynomial evaluation. We show that these two problems have constructions that are simultaneously efficient and error-correcting.
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https://hal.inria.fr/inria-00455571
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Submitted on : Wednesday, February 10, 2010 - 4:34:04 PM
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Victor Chen, Elena Grigorescu, Ronald de Wolf. Efficient and Error-Correcting Data Structures for Membership and Polynomial Evaluation. 27th International Symposium on Theoretical Aspects of Computer Science - STACS 2010, Inria Nancy Grand Est & Loria, Mar 2010, Nancy, France. pp.203-214. ⟨inria-00455571⟩

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