CloudBI: Practical Privacy-Preserving Outsourcing of Biometric Identification in the Cloud

Wang, Qian; Hu, Shengshan; Ren, Kui; He, Meiqi; Du, Minxin; Wang, Zhen

doi:10.1007/978-3-319-24177-7_10

Cited by 53 publications

(53 citation statements)

References 17 publications

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“…Matrix-based encryption is much faster than existing asymmetric biometric identification schemes based on encryption. The security property of this system, however, has been reviewed and found to be unsafe [24]. Therefore, the authors of [24] have proposed a security-based matrix-based biometric identification scheme called CloudBI [24].…”

Section: Related Workmentioning

confidence: 99%

“…The security property of this system, however, has been reviewed and found to be unsafe [24]. Therefore, the authors of [24] have proposed a security-based matrix-based biometric identification scheme called CloudBI [24]. First, they show that the database and identification requests are publicly disclosed because of the lack of randomness in encryption.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Implementation of an Image Processing Algorithm on a Platform DSP Tms320c6416

Tatar¹,

Machhout²

2017

ijacsa

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Abstract-In the context of emerging technologies, Cloud Computing (CC) was introduced as a new paradigm to host and deliver Information Technology Services. Cloud computing is a new model for delivering resources. However, there are many critical problems appeared with cloud computing, such as data privacy, security, and reliability, etc. But security is the most important between these problems. Biometric identification is a reliable and one of the easiest ways to recognize a person using extractable characteristics. In addition, a biometric application requires a fast and powerful processing systems, hence the increased use of embedded systems in biometric applications especially in image processing. Embedded systems have a wide variety and the choice of a well-designed processor is one of the most important factors that directly affect the overall performance of the system. This study highlights the performance of the Texas Instrument DSP for processing a biometric fingerprint recognition system.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Implementation of an Image Processing Algorithm on a Platform DSP Tms320c6416

Tatar¹,

Machhout²

2017

ijacsa

View full text Add to dashboard Cite

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“…However, such a scheme is not secure under an active attack where the cloud is able to collude with query users. Most recently, the authors in [6] proposed a secure outsourcing scheme that can defend against three different attacks as defined in [6], which include the attack where cloud and users are allowed to collude. The basic idea is to model the identification process as a Nearest Neighbor (NN)search problem.…”

Section: Introductionmentioning

confidence: 99%

“…Also, we point out that modelling the identification process as kNN search problem has inherent limitations in terms of template privacy. To be more specific, in [6], given two encrypted templates x and y and an encrypted query template z, the cloud can determine which template (x or y) is closer to z. Repeating this process, the cloud is able to identify the closet template to z.…”

Section: Introductionmentioning

confidence: 99%

Security and Privacy Enhancement for Outsourced Biometric Identification

Zhou

Ren

2018

2018 IEEE Global Communications Conference (GLOBECOM)

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A lot of research has been focused on secure outsourcing of biometric identification in the context of cloud computing. In such schemes, both the encrypted biometric database and the identification process are outsourced to the cloud. The ultimate goal is to protect the security and privacy of the biometric database and the query templates. Security analysis shows that previous schemes suffer from the enrolment attack and unnecessarily expose more information than needed. In this paper, we propose a new secure outsourcing scheme aims at enhancing the security from these two aspects. First, besides all the attacks discussed in previous schemes, our proposed scheme is also secure against the enrolment attack. Second, we model the identification process as a fixed radius similarity query problem instead of the kNN search problem. Such a modelling is able to reduce the exposed information thus enhancing the privacy of the biometric database. Our comprehensive security and complexity analysis show that our scheme is able to enhance the security and privacy of the biometric database and query templates while maintaining the same computational savings from outsourcing.

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“…Along another line, researches on privacypreserving computation on multimedia data begin to attract more and more attention in recent years. For example, [9] investigated face recognition on encrypted images, and [5,27] explored how to securely outsource computation of fingerprint identification to the cloud with privacy preservation.…”

Section: Introductionmentioning

confidence: 99%

SecHOG

Wang

et al. 2016

Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security

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Abundant multimedia data generated in our daily life has intrigued a variety of very important and useful real-world applications such as object detection and recognition etc. Accompany with these applications, many popular feature descriptors have been developed, e.g., SIFT, SURF and HOG. Manipulating massive multimedia data locally, however, is a storage and computation intensive task, especially for resourceconstrained clients. In this work, we focus on exploring how to securely outsource the famous feature extraction algorithm-Histogram of Oriented Gradients (HOG) to untrusted cloud servers, without revealing the data owner's private information. For the first time, we investigate this secure outsourcing computation problem under two different models and accordingly propose two novel privacy-preserving HOG outsourcing protocols, by efficiently encrypting image data by somewhat homomorphic encryption (SHE) integrated with single-instruction multiple-data (SIMD), designing a new batched secure comparison protocol, and carefully redesigning every step of HOG to adapt it to the ciphertext domain. Explicit Security and effectiveness analysis are presented to show that our protocols are practically-secure and can approximate well the performance of the original HOG executed in the plaintext domain. Our extensive experimental evaluations further demonstrate that our solutions achieve high efficiency and perform comparably to the original HOG when being applied to human detection.

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CloudBI: Practical Privacy-Preserving Outsourcing of Biometric Identification in the Cloud

Cited by 53 publications

References 17 publications

Implementation of an Image Processing Algorithm on a Platform DSP Tms320c6416

Implementation of an Image Processing Algorithm on a Platform DSP Tms320c6416

Security and Privacy Enhancement for Outsourced Biometric Identification

SecHOG

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