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Minshen Zhu "Exponential Lower Bounds for Locally Decodable and Correctable Codes for Insertions and Deletions"

Event Type
Seminar/Symposium
Sponsor
Theory Research Group, Department of Computer Science, University of Illinois
Location
4403 SC
Virtual
wifi event
Date
Oct 17, 2022   10:00 am  
Speaker
Minshen Zhu, PhD Candidate, Purdue University
Contact
Candice Steidinger
E-Mail
steidin2@illinois.edu
Phone
217-300-8564
Views
39
Originating Calendar
Computer Science Speakers Calendar

Abstract: 

Locally Decodable Codes (LDCs) are error-correcting codes for which individual message symbols can be quickly recovered despite errors in the codeword. LDCs for Hamming errors have been studied extensively in the past few decades, where a major goal is to understand the amount of redundancy that is necessary and sufficient to decode from large amounts of error, with small query complexity.

In this work, we study LDCs for insertion and deletion errors, called Insdel LDCs. Their study was initiated by Ostrovsky and Paskin-Cherniavsky (Information Theoretic Security, 2015), who gave a reduction from Hamming LDCs to Insdel LDCs with a small blowup in the code parameters. On the other hand, the only known lower bounds for Insdel LDCs come from those for Hamming LDCs, thus there is no separation between them. Here we prove new, strong lower bounds for the existence of Insdel LDCs. In particular, we show that 2-query linear Insdel LDCs do not exist, and give an exponential lower bound for the length of all q-query Insdel LDCs with constant q. For q≥3 our bounds are exponential in the existing lower bounds for Hamming LDCs. Furthermore, our exponential lower bounds continue to hold for adaptive decoders, and even in private-key settings where the encoder and decoder share secret randomness. This exhibits a strict separation between Hamming LDCs and Insdel LDCs.

Our strong lower bounds also hold for the related notion of Insdel LCCs (except in the private-key setting), due to an analogue to the Insdel notions of a reduction from Hamming LCCs to LDCs.

Our techniques are based on a delicate design and analysis of hard distributions of insertion and deletion errors, which depart significantly from typical techniques used in analyzing Hamming LDCs.

Based on joint work with Jeremiah Blocki, Kuan Cheng, Elena Grigorescu, Xin Li, and Yu Zheng.  FOCS'21.

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