Privacy-Preserving Face Recognition

By Z. Erkin, Martin Franz, Jorge Guajardo, Stefan Katzenbeisser, Reginald L. Lagendijk, Tomas Toft. International Symposium on Privacy Enhancing Technologies, 2009

621 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2668e789f2f8f62bcdcfcb7e9248a2238a57b94f

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In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

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 [6], [9]–[11], [13], [14], [17], [18], [25], [29].”
Efficient Privacy-Preserving Biometric Identification (2011, methodology): “For the underlying matching algorithm we could have used the same PCA (principal component analysis) technique used in several privacy-preserving face-recognition systems [5, 20]; however, this would have required a projection phase in addition to the distance-computation phase which would degrade p”
ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation (2015, neutral): “, for face-recognition [29], [35] or fingerprint-matching [10], [39].”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, background): “ collects inputs from different parties and returns the result of the target function to all (or a specific set of) parties. There are many applications in privacy-preserving biometric authentication [17, 40, 41, 76, 82], secure auctions [42], privacy-preserving machine learning [38], and data minin”
Privacy-preserving outsourced classification in cloud computing (2017, background): “For instance, Erkin et al. [15] used an additive homomorphic property (Paillier cryptosystem) to support the secure computation and realized a privacy-preserving face recognition.”
Faster Secure Two-Party Computation Using Garbled Circuits (2011, background): “Beginning with Fairplay [22], several implementations of generic secure two-party computation have been developed in the past few years [11, 21, 27] and used to build privacy-preserving protocols for various functions (e.g., [4,13,16,26,29]).”
Efficient Privacy-Preserving Face Recognition (2009, positive): “In the most recent proposal for privacy-preserving face recognition [12] the authors use the standard and popular Eigenface [34,33] recognition algorithm and design a protocol that performs operations on encrypted images by means of homomorphic encryption schemes, more concretely, Pailler [29,11] as”
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study (2018, background): “A few cryptographic solutions [13,66] were developed to locally encrypt visual information in a homomorphic way, i.”
ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (2020, methodology): “This method is used for various traits of biometrics such as face-recognition [42, 49] and fingerprint-matching [15, 51].”
Privacy Preserving Image-Based Localization (2019, background): “Other applications include image retrieval [66], face recognition [22], video surveillance [75],”
Machine Learning Classification over Encrypted Data (2015, methodology): “There are at least two approaches to performing comparison efficiently: using specialized homomorphic encryption [30], [31], [17], [32], or using garbled circuits [46].”
More efficient oblivious transfer and extensions for faster secure computation (2013, neutral): “Securely computing the minimum of a set of values is a common building block in privacy-preserving protocols and is used to ﬁnd best matches, e.g., for face recognition [11] or online marketplaces [8].”
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption (2017, background): “Another relevant topic is privacy-preserving face retrieval based on homomorphic encryption [18, 50].”
Face/Off: Preventing Privacy Leakage From Photos in Social Networks (2015, background): “Fortunately, there is active research towards privacy-preserving face recognition [24,36] and, therefore, we envision that this very eﬀective technology will be adopted by such services.”
CloudID: Trustworthy cloud-based and cross-enterprise biometric identification (2015, methodology): “Both of these protocols (Barni et al., 2010; Erkin et al., 2009) rely on homomorphic encryption and do not try to ﬁnd the speciﬁc match but the group of the nearest matches within some threshold.”
Image Feature Extraction in Encrypted Domain With Privacy-Preserving SIFT (2012, methodology): “…both robustness and security, will find many applications, including media retrieval [3], [6], [7], media authentication [8], face identification [9], face recognition [10]–[13], fingerprint verification [14], and video-based mobile location search [15], for the purpose of preserving privacy.”