skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Eyes-closed versus eyes-open differences in spontaneous neural dynamics during development
Award ID(s):
2112455
PAR ID:
10569673
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
NeuroImage
Volume:
258
Issue:
C
ISSN:
1053-8119
Page Range / eLocation ID:
119337
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
  2. 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 oblivious 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 which map keywords to values. One computes many LSHs of each record in the database and uses these hashes as keywords in the oblivious 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 oblivious map needs to answer disjunction queries efficiently. 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 zeroleakage 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. 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 synthetic iris database, a search requires 18 rounds of communication and 25ms of parallel computation. Our scheme is implemented and open-sourced. 
    more » « less
  3. 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 oblivious 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 which map keywords to values. One computes many LSHs of each record in the database and uses these hashes as keywords in the oblivious 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 oblivious map needs to answer disjunction queries efficiently. 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. For the largest tested parameters of a 5000 synthetic iris database, a search requires 18 rounds of communication and 25ms of parallel computation. Our scheme is implemented and open-sourced. 
    more » « less
  4. null (Ed.)