The Experts below are selected from a list of 10632 Experts worldwide ranked by ideXlab platform
Hui Li - One of the best experts on this subject based on the ideXlab platform.
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Efficient and Privacy-preserving Online Fingerprint Authentication Scheme Over Outsourced Data
IEEE Transactions on Cloud Computing, 2018Co-Authors: Xiaopeng Yang, Rongxing Lu, Hui LiAbstract:With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric Data bring out increasing concerns due to the highly sensitivity of biometric Data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted Outsourced Data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be Outsourced to different servers with user's authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology for secure Euclidean distance calculation to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode Data in the outsourcing scenarios. Through detailed security analysis, we show that e-Finga can resist various security threats. In addition, we implement e-Finga over a workstation with a real fingerprint Database, and extensive simulation results demonstrate that the proposed e-Finga scheme can serve efficient and accurate online fingerprint authentication.
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ICC - Achieve efficient and privacy-preserving online fingerprint authentication over encrypted Outsourced Data
2017 IEEE International Conference on Communications (ICC), 2017Co-Authors: Rongxing Lu, Hui LiAbstract:With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric Data bring out increasing concerns due to the highly sensitivity of biometric Data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted Outsourced Data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be Outsourced to different servers with user's authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology is introduced to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode Data in the outsourcing scenarios. Through detailed analysis, we show that e-Finga can resist various security threats and serve efficient and accurate online fingerprint authentication.
Sanjay Jha - One of the best experts on this subject based on the ideXlab platform.
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secure and light weight fine grained access mechanism for Outsourced Data
Trust Security And Privacy In Computing And Communications, 2017Co-Authors: Mosarrat Jahan, Partha Sarathi Roy, Kouichi Sakurai, Aruna Seneviratne, Sanjay JhaAbstract:In this paper we explore the problem of providing selective read/write access to the Outsourced Data for clients using mobile devices in an environment that supports users from multiple domains and where attributes are generated by multiple authorities. We consider Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme as it can provide access control on the encrypted Outsourced Data. One limitation of CP-ABE is that users can modify the access policy specified by the Data owner if write operations are introduced in the scheme. We propose a protocol for providing different levels of access to Outsourced Data that permits the authorized users to perform write operation without altering the access policy specified by the Data owner. Our scheme provides fine-grained read/write access to the users, accompanied with a light weight signature scheme and computationally inexpensive user revocation mechanism suitable for resource-constrained mobile devices. The security analysis demonstrates the robustness of the proposed scheme.
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method for providing secure and private fine grained access to Outsourced Data
Local Computer Networks, 2015Co-Authors: Mosarrat Jahan, Aruna Seneviratne, Mohsen Rezvani, Sanjay JhaAbstract:Outsourcing Data to the cloud for computation and storage has been on rise in recent years. In this paper we investigate the problem of supporting write operation on the Outsourced Data for clients using mobile devices. We consider the Attribute-based Encryption (ABE) scheme as it is well suited to support access control in Outsourced cloud environment. Currently there is a gap in the literature on providing write access on the Data encrypted with ABE. Moreover, since ABE is computationally expensive, it imposes processing burden on resource constrained mobile devices. Our work has two fold advantages. Firstly, we extend the single authority Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme to support write operations. Secondly, in achieving this goal, we move some of the expensive computations to a manager and remote cloud server by exploiting their high-end computational power. Our security analysis demonstrates that the security properties of system are not compromised.
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LCN - Method for providing secure and private fine-grained access to Outsourced Data
2015 IEEE 40th Conference on Local Computer Networks (LCN), 2015Co-Authors: Mosarrat Jahan, Aruna Seneviratne, Mohsen Rezvani, Sanjay JhaAbstract:Outsourcing Data to the cloud for computation and storage has been on rise in recent years. In this paper we investigate the problem of supporting write operation on the Outsourced Data for clients using mobile devices. We consider the Attribute-based Encryption (ABE) scheme as it is well suited to support access control in Outsourced cloud environment. Currently there is a gap in the literature on providing write access on the Data encrypted with ABE. Moreover, since ABE is computationally expensive, it imposes processing burden on resource constrained mobile devices. Our work has two fold advantages. Firstly, we extend the single authority Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme to support write operations. Secondly, in achieving this goal, we move some of the expensive computations to a manager and remote cloud server by exploiting their high-end computational power. Our security analysis demonstrates that the security properties of system are not compromised.
Xiaopeng Yang - One of the best experts on this subject based on the ideXlab platform.
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Efficient and Privacy-preserving Online Fingerprint Authentication Scheme Over Outsourced Data
IEEE Transactions on Cloud Computing, 2018Co-Authors: Xiaopeng Yang, Rongxing Lu, Hui LiAbstract:With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric Data bring out increasing concerns due to the highly sensitivity of biometric Data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted Outsourced Data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be Outsourced to different servers with user's authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology for secure Euclidean distance calculation to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode Data in the outsourcing scenarios. Through detailed security analysis, we show that e-Finga can resist various security threats. In addition, we implement e-Finga over a workstation with a real fingerprint Database, and extensive simulation results demonstrate that the proposed e-Finga scheme can serve efficient and accurate online fingerprint authentication.
Rongxing Lu - One of the best experts on this subject based on the ideXlab platform.
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Efficient and Privacy-preserving Online Fingerprint Authentication Scheme Over Outsourced Data
IEEE Transactions on Cloud Computing, 2018Co-Authors: Xiaopeng Yang, Rongxing Lu, Hui LiAbstract:With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric Data bring out increasing concerns due to the highly sensitivity of biometric Data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted Outsourced Data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be Outsourced to different servers with user's authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology for secure Euclidean distance calculation to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode Data in the outsourcing scenarios. Through detailed security analysis, we show that e-Finga can resist various security threats. In addition, we implement e-Finga over a workstation with a real fingerprint Database, and extensive simulation results demonstrate that the proposed e-Finga scheme can serve efficient and accurate online fingerprint authentication.
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ICC - Achieve efficient and privacy-preserving online fingerprint authentication over encrypted Outsourced Data
2017 IEEE International Conference on Communications (ICC), 2017Co-Authors: Rongxing Lu, Hui LiAbstract:With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric Data bring out increasing concerns due to the highly sensitivity of biometric Data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted Outsourced Data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be Outsourced to different servers with user's authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology is introduced to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode Data in the outsourcing scenarios. Through detailed analysis, we show that e-Finga can resist various security threats and serve efficient and accurate online fingerprint authentication.
Anwitaman Datta - One of the best experts on this subject based on the ideXlab platform.
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Write-only oblivious RAM-based privacy-preserved access of Outsourced Data
International Journal of Information Security, 2017Co-Authors: Lichun Li, Anwitaman DattaAbstract:Data outsourcing is plagued with several security and privacy concerns. Oblivious RAM (ORAM) can be used to address one of the many concerns, specifically to protect the privacy of Data access pattern from Outsourced cloud storage. This is achieved by simulating each original read or write operation with some read and write operations on both real and dummy Data items. This paper proposes two single-server write-only ORAM schemes and one multi-server scheme, which simulate only the write operations and protect only the write pattern. The reduction in functionality however allows to build much simpler and efficient (in terms of communication/storage cost) ORAMs. Our schemes can achieve constant communication cost with acceptable storage usage. Write-only ORAM can be used in two situations: (i) only the write pattern is considered to contain sensitive information and needs protection. (ii) In Outsourced Data sharing, ORAM cannot be used to protect read pattern anyway due to access control issues, and Private Information Retrieval (PIR) has to be used instead. In this paper, we also study how to augment ORAM to support the use of PIR in the latter situation.
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Write-Only Oblivious RAM based Privacy-Preserved Access of Outsourced Data.
IACR Cryptology ePrint Archive, 2013Co-Authors: Anwitaman DattaAbstract:Oblivious RAM (ORAM) has recently attracted a lot of interest since it can be used to protect the privacy of Data user’s Data access pattern from (honest but curious) Outsourced storage. This is achieved by simulating each original Data read or write operation with some read and write operations on some real and dummy Data items. This paper proposes two single-server writeonly ORAM schemes and one multi-server write-only ORAM scheme, which simulate only the write operations and protect only the write pattern. The reduction of functions however allows to build much simpler and efficient (in terms of communication cost and storage usage) write-only ORAMs. Write-only ORAM can be used in conjunction with Private Information Retrieval (PIR), which is a technique to protect Data user’s read patterns, in order to protect both write and read patterns. Write-only ORAM may be used alone too, when only write patterns need protection. We study two usage scenarios: (i) Data publishing/sharing: where a Data owner shares the Data with others, who only consume the published information. Data consumers should not have write access to the Outsourced Data, and thus cannot use ORAM to protect their read patterns in this scenario. To hide access patterns from the Outsourced storage, the Data owner can use ORAM to write Data, and Data consumers use PIR to read Data. Alternatively, for some applications, a Data consumer can trivially download all Data once or regularly, and neither the Data owner nor Data consumers mind that the Outsourced storage learns such read pattern. Compared with using traditional ORAM, using the simpler write-only ORAM here produces much less communication cost and/or clientside storage usage. Our single-server write-only ORAM scheme produces lower (typically one order lower) communication cost with the same client-side storage usage, or requires much less (typically at least one order less) client-side storage to achieve the same level of communication cost than the best known single-server full functional ORAM schemes do. Compared with the best known multi-server ORAM scheme, our write-only ORAM schemes have lower (typically one order lower) communication cost, or achieve the same communication cost with the same client-side storage usage in single-server setting. (ii) the Data owner’s personal use: Our writeonly ORAM schemes combined with PIR can be used as building blocks for some existing full functional ORAM schemes. This leads to the reduction of the communication costs for two fullfunctional ORAM schemes by the factors of O(logN) and O( √ logN × log logN), where N is the maximum Data item count. One of these resulting schemes has a communication cost of O(l), where l is Data item length. This is typically one order lower than the previous best known ORAM scheme’s cost, which is O(logN × l). The other resulting scheme also achieves O(logN × l) communication cost, but its client-side storage usage is several orders lower than the best known single-server ORAM’s.