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

Victor Chen, Elena Grigorescu & Ronald De Wolf
We construct efficient data structures that are resilient against a constant fraction of adversarial noise. Our model requires that the decoder answers \emph{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 \emph{all} queries correctly with high probability. Our model is the common generalization of an error-correcting data structure model...
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