Sequential Random Sampling PIR with Multiple Colluding Servers in DNA-Based Data Storage
Abstract
As DNA-based data storage evolves, protecting user privacy during data retrieval has become increasingly important. We study sequential random sampling DNA private information retrieval (SRS DNA PIR) with multiple colluding random sampling servers, where the database is partitioned into servers of equal size. We investigate the tradeoff between the download cost, defined as the expected number of queries, and the privacy leakage, measured by mutual information. We derive lower bounds on this tradeoff, including a bound given by an optimization problem. This bound is tight when each server stores two files, and we construct schemes that attain it. For servers of any size, we construct schemes that apply a single-server scheme to a randomly selected subset of servers.
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BibTeX
@misc{wang2026sequential,
title = {{Sequential Random Sampling PIR with Multiple Colluding Servers in DNA-Based Data Storage}},
author = {Chen Wang and Natalia Silberstein and Eitan Yaakobi},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.10211},
eprint = {2610.10211},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.10211},
}
RIS
TY - UNPB
TI - Sequential Random Sampling PIR with Multiple Colluding Servers in DNA-Based Data Storage
AU - Chen Wang
AU - Natalia Silberstein
AU - Eitan Yaakobi
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.10211
UR - https://arxiv.org/abs/2610.10211
AB - As DNA-based data storage evolves, protecting user privacy during data retrieval has become increasingly important. We study sequential random sampling DNA private information retrieval (SRS DNA PIR) with multiple colluding random sampling servers, where the database is partitioned into servers of equal size. We investigate the tradeoff between the download cost, defined as the expected number of queries, and the privacy leakage, measured by mutual information. We derive lower bounds on this tradeoff, including a bound given by an optimization problem. This bound is tight when each server stores two files, and we construct schemes that attain it. For servers of any size, we construct schemes that apply a single-server scheme to a randomly selected subset of servers.
ER -