Efficient Privacy-Preserving Biometric Identification

By Yan Huang, Lior Malka, David E. Evans, Jonathan Katz. 2011

198 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013, methodology): “Similar to our system, some works introduced hybrid approaches, such as combining HE and garbled circuits for face and fingerprints recognition [51], [52], and combining secret sharing with garbled circuits for learning a decision tree [53].”
I-Pic: A Platform for Privacy-Compliant Image Capture (2016, methodology): “Optimized n x 1 secure dot product: I-Pic reduces the number of encryptions from nm to m using ideas from [21].”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, background): “There are many applications in privacy-preserving biometric authentication [17, 40, 41, 76, 82], secure auctions [42], privacy-preserving machine learning [38], and data mining [63, 64, 74].”
ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation (2015, methodology): “Mixed-protocols have been used for several privacy-preserving applications, such as medical diagnostics [3], fingerprint recognition [39], iris- and fingercode authentication [10], ridge-regression [60], computation on non-integers and Hidden Markov Models [31], and matrix factorization [59].”
More efficient oblivious transfer and extensions for faster secure computation (2013, background): “, [26,27,29,30,50].”
Billion-Gate Secure Computation with Malicious Adversaries (2012, result): “Our results are in the standard model (i.e., no common reference strings or PKIs) and, in contrast to prior work, we do not use the random oracle model which has well-established theoretical shortcomings.”
ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (2020, methodology): “Since Maxpool/Minpool is an essential building block for several applications like K-means clustering [25], face-recognition [93], and fingerprint-matching [15, 43], we provide a separate analysis for Maxpool in §6.”
Efficient Secure Computation with Garbled Circuits (2011, methodology): “In privacy-preserving fingerprint matching [29], a client has a scanned candidate fingerprint and the server has a database of fingerprint images with associated profile information.”
Privacy-preserving matrix factorization (2013, background): “, face and fingerprints recognition [57, 26], and learning a decision tree [41]; such discrete function evaluations differ considerably from matrix factorization.”
Faster Secure Two-Party Computation Using Garbled Circuits (2011, unknown): “, [4,13,16,26,29]).”
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models (2018, background): “Another relevant line of work is privacy-preserving machine learning [24], [19], [11], [10], [14], [28], [5], [6], [1].”
TinyGarble: Highly Compressed and Scalable Sequential Garbled Circuits (2015, background): “Contemporary literature has cited multiple important privacy preserving and security critical applications that could benefit from a practical realization of SFE, including but not limited to: biometrics matching, face recognition, image/data classification, electronic auctions and voting, remote di”
Quid-Pro-Quo-tocols: Strengthening Semi-honest Protocols with Dual Execution (2012, background): “To obtain reasonable performance, however, many of these works [29, 14, 16] and others that build on them [17, 15] assume the semihonest (or honest-but-curious) model in which the adversary is assumed to always follow the protocol but may try to”