Privacy Protection in Biometric-Based Recognition Systems: A marriage between cryptography and signal processing

Barni, Mauro; Droandi, Giulia; Lazzeretti, Riccardo

doi:10.1109/msp.2015.2438131

Cited by 79 publications

(39 citation statements)

References 39 publications

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“…Secure multiparty computation techniques are proposed in the context of one-to-one verification to protect biometric similarity evaluations [5], [19], [20], [21]. They are mostly based on combining cryptographic primitives such as homomorphic encryption and garbled circuits to process encrypted biometric data without the need of decrypting them.…”

Section: Introductionmentioning

confidence: 99%

Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification

Wang

Wan

Guo

et al. 2018

IEEE Trans. Pattern Anal. Mach. Intell.

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Similarity search is essential to many important applications and often involves searching at scale on high-dimensional data based on their similarity to a query. In biometric applications, recent vulnerability studies have shown that adversarial machine learning can compromise biometric recognition systems by exploiting the biometric similarity information. Existing methods for biometric privacy protection are in general based on pairwise matching of secured biometric templates and have inherent limitations in search efficiency and scalability. In this paper, we propose an inference-based framework for privacy-preserving similarity search in Hamming space. Our approach builds on an obfuscated distance measure that can conceal Hamming distance in a dynamic interval. Such a mechanism enables us to systematically design statistically reliable methods for retrieving most likely candidates without knowing the exact distance values. We further propose to apply Montgomery multiplication for generating search indexes that can withstand adversarial similarity analysis, and show that information leakage in randomized Montgomery domains can be made negligibly small. Our experiments on public biometric datasets demonstrate that the inference-based approach can achieve a search accuracy close to the best performance possible with secure computation methods, but the associated cost is reduced by orders of magnitude compared to cryptographic primitives.

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Section: Introductionmentioning

confidence: 99%

Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification

Wang

Wan

Guo

et al. 2018

IEEE Trans. Pattern Anal. Mach. Intell.

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“…In [29] and [30], the authors provide an overview of several biometric template protection schemes based on homomorphic encryption and garbled circuits. Barni et al [22] present a way to protect fixed-length fingercodes [31] using homomorphic encryption.…”

Section: Related Workmentioning

confidence: 99%

Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters

Nautsch¹,

Isadskiy²,

Kolberg³

et al. 2018

The Speaker and Language Recognition Workshop (Odyssey 2018)

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Data privacy is crucial when dealing with biometric data. Accounting for the latest European data privacy regulation and payment service directive, biometric template protection is essential for any commercial application. Ensuring unlinkability across biometric service operators, irreversibility of leaked encrypted templates, and renewability of e.g., voice models following the i-vector paradigm, biometric voice-based systems are prepared for the latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean and cosine comparators are known to ensure data privacy demands, without loss of discrimination nor calibration performance. Bridging gaps from template protection to speaker recognition, two architectures are proposed for the two-covariance comparator, serving as a generative model in this study. The first architecture preserves privacy of biometric data capture subjects. In the second architecture, model parameters of the comparator are encrypted as well, such that biometric service providers can supply the same comparison modules employing different key pairs to multiple biometric service operators. An experimental proof-of-concept and complexity analysis is carried out on the data from the 2013 -2014 NIST i-vector machine learning challenge.

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“…However, it is a nightmare once a biometric is compromised, e.g., it can be used to impersonate the victims, to break the privacy of the victims. In order to protect privacy introduced by biometric based security systems, securer multi-party computation techniques are usually hired to execute the job [58] [59].…”

Section: Biometric Privacymentioning

confidence: 99%

Big Privacy: Challenges and Opportunities of Privacy Study in the Age of Big Data

2016

IEEE Access

284

131

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One of the biggest concerns of big data is privacy. However, the study on big data privacy is still at a very early stage. We believe the forthcoming solutions and theories of big data privacy root from the in place research output of the privacy discipline. Motivated by these factors, we extensively survey the existing research outputs and achievements of the privacy field in both application and theoretical angles, aiming to pave a solid starting ground for interested readers to address the challenges in the big data case. We firstly present an overview of the battle ground by defining the roles and operations of privacy systems. Secondly, we review the milestones of the current two major research categories of privacy: data clustering and privacy frameworks. Thirdly, we discuss the effort of privacy study from the perspectives of different disciplines, respectively. Fourthly, the mathematical description, measurement, and modeling on privacy are presented. We summarize the challenges and opportunities of this promising topic at the end of the article, hoping to shed light on the exciting and almost uncharted land.

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Privacy Protection in Biometric-Based Recognition Systems: A marriage between cryptography and signal processing

Cited by 79 publications

References 39 publications

Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification

Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification

Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters

Big Privacy: Challenges and Opportunities of Privacy Study in the Age of Big Data

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