Paper 2023/736

Private Eyes: Zero-Leakage Iris Searchable Encryption

Julie Ha, Boston University
Chloe Cachet, University of Connecticut
Luke Demarest, University of Connecticut
Sohaib Ahmad, University of Connecticut
Benjamin Fuller, University of Connecticut
Abstract

This work introduces Private Eyes, the first zero-leakage biometric database. The only leakage of the system is unavoidable: 1) the log of the dataset size and 2) the fact that a query occurred. Private Eyes is built from symmetric searchable encryption. Approximate proximity queries are used: given a noisy reading of a biometric, the goal is to retrieve all stored records that are close enough according to a distance metric. Private Eyes combines locality sensitive-hashing or LSHs (Indyk and Motwani, STOC 1998) and oblivious maps. One computes many LSHs of each record in the database, and uses these hashes as keys in an encrypted map with the matching biometric readings concatenated as the value. At search time with a noisy reading, one computes the LSHs, and retrieves the disjunction of the resulting values from the map. The underlying encrypted map needs to efficiently answer disjunction queries. We focus on the iris biometric which requires a large number of LSHs, approximately $1000$. Boldyreva and Tang's (PoPETS 2021) design yields a suitable map for a small number of LSHs (their application was in zero-leakage $k$-nearest-neighbor search). Our solution is a zero-leakage disjunctive map designed for the setting when most clauses do not match any records. For the iris, on average at most $6\%$ of LSHs match any stored value. Our scheme is implemented and open-sourced. We evaluate using the ND-0405 dataset; this dataset has $356$ irises suitable for testing. To scale our evaluation, we use a generative adversarial network to produce synthetic irises. Accurate statistics on sizes beyond available datasets is crucial to optimizing the cryptographic primitives. This tool may be of independent interest. For the largest tested parameters of a $5000$ iris database, search requires $26$ rounds of communication and $26$ minutes of single-threaded computation.

Note: Clarified notation and definitions.

Metadata
Available format(s)
PDF
Category
Cryptographic protocols
Publication info
Preprint.
Keywords
Searchable EncryptionBiometricsProximity Search
Contact author(s)
hajulie @ bu edu
chloe cachet @ uconn edu
luke h demarest @ gmail com
sohaib ahmad @ uconn edu
benjamin fuller @ uconn edu
History
2024-09-10: last of 3 revisions
2023-05-22: received
See all versions
Short URL
https://ia.cr/2023/736
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2023/736,
      author = {Julie Ha and Chloe Cachet and Luke Demarest and Sohaib Ahmad and Benjamin Fuller},
      title = {Private Eyes: Zero-Leakage Iris Searchable Encryption},
      howpublished = {Cryptology {ePrint} Archive, Paper 2023/736},
      year = {2023},
      url = {https://eprint.iacr.org/2023/736}
}
Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.