Re-identification attacks—A systematic literature review

Henriksen-Bulmer, Jane; Jeary, Sherry

doi:10.1016/j.ijinfomgt.2016.08.002

Cited by 61 publications

(44 citation statements)

References 37 publications

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“…(1) Identification attack: Identification attack identifies a user's name or identity-based on some public dataset [76]. It is also called re-identification [53,58] when anonymisation is reversed. Such kind of attack is illustrated in Fig.…”

Section: Attack and Threat Modelsmentioning

confidence: 99%

When Machine Learning Meets Privacy

et al. 2021

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The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.

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Section: Attack and Threat Modelsmentioning

confidence: 99%

When Machine Learning Meets Privacy

et al. 2021

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“…• Linkage attacks: The last scenario assumes an attacker is able to link the privacy enhanced data with other sources of information that, in combination, reveal sensitive details bout the original data. These sources of information can represent parts of the biometric data not considered critical from a privacy perspective, prior knowledge, multiple instances of the same privacy-enhanced data, or external sources of information, for which a link with the privacy-enhanced data can be established [23], [74], [268], [269].…”

Section: Evaluating Robustnessmentioning

confidence: 99%

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Meden

Rot

Terhörst

et al. 2021

IEEE Trans.Inform.Forensic Secur.

126

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Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy-enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy-related research in the area of biometrics and review existing work on Biometric Privacy-Enhancing Techniques (B-PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B-PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future.

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“…PISS emphasizes that all collected personal data should be immediately de-identified and stored separately from their profile data 9,10 . However, even after the de-identification, anonymized personal data still face re-identification risk and are vulnerable to linkage attacks launched by either honest but curious data collectors or malicious hackers 11,12 . Therefore, the re-identification risk of individual data not only reflects the privacy risk level of individuals but also supports regulators in formulating privacy protection policies.…”

Section: Introductionmentioning

confidence: 99%

Re-identification risk prediction paradigm using incomplete statistical information and recursive hypergeometric distribution

Yang

Wang

et al. 2021

Preprint

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The dataset anonymization has not eliminated the re-identification risk, the evaluation of which remains a huge challenge, especially given incomplete statistical information. The re-identification risk of individuals depends on their tuple frequency. The paper proposes the recursive hypergeometric (RH) distribution to accurately calculate the tuple frequency and leverages the binomial distribution to approximate the RH distribution and to efficiently predict the re-identification risk of individuals in both generated and real-world datasets. The experimental results show that our tuple frequency based re-identification risk (TFRR) prediction model has a superior performance (average AUC 0.86~0.98) for all types of datasets. Furthermore, we exploit the value dependence knowledge to rectify the prediction result for some subsets (average AUC 0.95~0.98). Our research reveals the general rule of the tuple frequency distribution and enables individuals and regulators to responsively predict the re-identification risk.

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Re-identification attacks—A systematic literature review

Cited by 61 publications

References 37 publications

When Machine Learning Meets Privacy

When Machine Learning Meets Privacy

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Re-identification risk prediction paradigm using incomplete statistical information and recursive hypergeometric distribution

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