Secure Outsourced Biometric Authentication With Performance Evaluation on Smartphones

Šeděnka, Jaroslav; Govindarajan, Sathya; Gasti, Paolo; Balagani, Kiran S.

doi:10.1109/tifs.2014.2375571

Cited by 39 publications

(27 citation statements)

References 33 publications

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“…The performance of all classifiers was evaluated on four different datasets using false accept rate (FAR), false reject rate (FRR), Half Total Error Rates (HTER), and Area Under the Curve (AUC) [16,28,29]. HTER is defined as the average of FAR and FRR.…”

Section: Performance Evaluationmentioning

confidence: 99%

Continuous authentication using one-class classifiers and their fusion

Kumar

Kundu

Phoha

2018

2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)

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While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances.Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (M CC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of M CC. Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.

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Section: Performance Evaluationmentioning

confidence: 99%

Continuous authentication using one-class classifiers and their fusion

Kumar

Kundu

Phoha

2018

2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)

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“…Many existing contextual authentication proposals, summarised in Table 1, use device measurements in unprotected form-giving rise to privacy concerns especially when authentication decisions are outsourced to a remote server [8,23,28]. As noted previously, studies have already shown that users are generally reluctant towards disclosing, in particular, their GPS location to device applications [3][4][5]9].…”

Section: Discussionmentioning

confidence: 99%

“…Paillier [19], and garbled circuits for two-party computation (2PC) [8,12,23,28]. However, challenges still remain with respect computational complexity and storage overhead, which is exemplified by worst cases of 31.2s and 45.9s to compute authentication decisions in [8] and [28] respectively. Storage complexity is also an issue, with 4-175MB required for computing a single authentication decision in [28].…”

Section: Discussionmentioning

confidence: 99%

“…However, challenges still remain with respect computational complexity and storage overhead, which is exemplified by worst cases of 31.2s and 45.9s to compute authentication decisions in [8] and [28] respectively. Storage complexity is also an issue, with 4-175MB required for computing a single authentication decision in [28]. Homomorphic encryption-based schemes also face inherent challenges regarding the supported arithmetic operations for learning from data, i.e.…”

Section: Discussionmentioning

confidence: 99%

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Privacy-Enhancing Context Authentication from Location-Sensitive Data

Mainali¹,

Shepherd²,

Petitcolas³

2019

Proceedings of the 14th International Conference on Availability, Reliability and Security

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This paper proposes a new privacy-enhancing, context-aware user authentication system, ConSec, which uses a transformation of general location-sensitive data, such as GPS location, barometric altitude and noise levels, collected from the user's device, into a representation based on locality-sensitive hashing (LSH). The resulting hashes provide a dimensionality reduction of the underlying data, which we leverage to model users' behaviour for authentication using machine learning. We present how ConSec supports learning from categorical and numerical data, while addressing a number of on-device and network-based threats. ConSec is implemented subsequently for the Android platform and evaluated using data collected from 35 users, which is followed by a security and privacy analysis. We demonstrate that LSH presents a useful approach for context authentication from location-sensitive data without directly utilising plain measurements. CCS CONCEPTS• Security and privacy → Authentication; Privacy-preserving protocols; Mobile and wireless security.

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“…Recently,Sedȇnka et. al proposed another secure protocol for biometric comparison [42]. They reported various time costs according to different settings.…”

Section: Compare With State-of-the-artmentioning

confidence: 99%

Privacy-Preserving Outsourced Media Search

Weng

Amsaleg

Furon

2016

IEEE Trans. Knowl. Data Eng.

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International audienceThis work proposes a privacy-protection framework for an important application called outsourced media search. This scenario involves a data owner, a client, and an untrusted server, where the owner outsources a search service to the server. Due to lack of trust, the privacy of the client and the owner should be protected. The framework relies on multimedia hashing and symmetric encryption. It requires involved parties to participate in a privacy-enhancing protocol. Additional processing steps are carried out by the owner and the client: (i) before outsourcing low-level media features to the server, the owner has to one-way hash them, and partially encrypt each hash-value; (ii) the client completes the similarity search by re-ranking the most similar candidates received from the server. One-way hashing and encryption add ambiguity to data and make it difficult for the server to infer contents from database items and queries, so the privacy of both the owner and the client is enforced. The proposed framework realizes trade-offs among strength of privacy enforcement, quality of search, and complexity, because the information loss can be tuned during hashing and encryption. Extensive experiments demonstrate the effectiveness and the flexibility of the framework

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Secure Outsourced Biometric Authentication With Performance Evaluation on Smartphones

Cited by 39 publications

References 33 publications

Continuous authentication using one-class classifiers and their fusion

Continuous authentication using one-class classifiers and their fusion

Privacy-Enhancing Context Authentication from Location-Sensitive Data

Privacy-Preserving Outsourced Media Search

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