The Experts below are selected from a list of 252 Experts worldwide ranked by ideXlab platform
Liehuang Zhu - One of the best experts on this subject based on the ideXlab platform.
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An Efficient and Privacy-Preserving Biometric Identification Scheme in Cloud Computing
IEEE Access, 2018Co-Authors: Liehuang Zhu, Chuan Zhang, Ximeng Liu, Cheng HuangAbstract:Biometric identification has become increasingly popular in recent years. With the development of cloud computing, Database Owners are motivated to outsource the large size of biometric data and identification tasks to the cloud to get rid of the expensive storage and computation costs, which, however, brings potential threats to users’ privacy. In this paper, we propose an efficient and privacy-preserving biometric identification outsourcing scheme. Specifically, the biometric To execute a biometric identification, the Database Owner encrypts the query data and submits it to the cloud. The cloud performs identification operations over the encrypted Database and returns the result to the Database Owner. A thorough security analysis indicates that the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud. Compared with previous protocols, experimental results show that the proposed scheme achieves a better performance in both preparation and identification procedures.
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PTBI: An efficient privacy-preserving biometric identification based on perturbed term in the cloud
Information Sciences, 2017Co-Authors: Liehuang Zhu, Chang XuAbstract:Biometric identification has played an important role in achieving user authentication. For efficiency and economic savings, biometric data Owners are motivated to outsource the biometric data and identification tasks to a third party, which however introduces potential threats to user's privacy. In this paper, we propose a new privacy-preserving biometric identification scheme which can release the Database Owner from heavy computation burden. In the proposed scheme, we design concrete biometric data encryption and matching algorithms, and introduce perturb terms in each biometric data. A thorough analysis indicates that our schemes are secure, and the ultimate scheme offers a high level of privacy protection. In addition, the performance evaluations via extensive simulations demonstrate our schemes’ efficiency.
Chang Xu - One of the best experts on this subject based on the ideXlab platform.
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PTBI: An efficient privacy-preserving biometric identification based on perturbed term in the cloud
Information Sciences, 2017Co-Authors: Liehuang Zhu, Chang XuAbstract:Biometric identification has played an important role in achieving user authentication. For efficiency and economic savings, biometric data Owners are motivated to outsource the biometric data and identification tasks to a third party, which however introduces potential threats to user's privacy. In this paper, we propose a new privacy-preserving biometric identification scheme which can release the Database Owner from heavy computation burden. In the proposed scheme, we design concrete biometric data encryption and matching algorithms, and introduce perturb terms in each biometric data. A thorough analysis indicates that our schemes are secure, and the ultimate scheme offers a high level of privacy protection. In addition, the performance evaluations via extensive simulations demonstrate our schemes’ efficiency.
Santhosh G Kumar - One of the best experts on this subject based on the ideXlab platform.
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an effective private data storage and retrieval system using secret sharing scheme based on secure multi party computation
arXiv: Cryptography and Security, 2015Co-Authors: Divya G Nair, V P Binu, Santhosh G KumarAbstract:Privacy of the outsourced data is one of the major challenge.Insecurity of the network environment and untrustworthiness of the service providers are obstacles of making the Database as a service.Collection and storage of personally identifiable information is a major privacy concern.On-line public Databases and resources pose a significant risk to user privacy, since a malicious Database Owner may monitor user queries and infer useful information about the customer.The challenge in data privacy is to share data with third-party and at the same time securing the valuable information from unauthorized access and use by third party.A Private Information Retrieval(PIR) scheme allows a user to query Database while hiding the identity of the data retrieved.The naive solution for confidentiality is to encrypt data before outsourcing.Query execution,key management and statistical inference are major challenges in this case.The proposed system suggests a mechanism for secure storage and retrieval of private data using the secret sharing technique.The idea is to develop a mechanism to store private information with a highly available storage provider which could be accessed from anywhere using queries while hiding the actual data values from the storage provider.The private information retrieval system is implemented using Secure Multi-party Computation(SMC) technique which is based on secret sharing. Multi-party Computation enable parties to compute some joint function over their private inputs.The query results are obtained by performing a secure computation on the shares owned by the different servers.
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an effective private data storage and retrieval system using secret sharing scheme based on secure multi party computation
International Conference on Data Science and Engineering, 2014Co-Authors: Divya G Nair, V P Binu, Santhosh G KumarAbstract:Privacy of the outsourced data is one of the major challenge.Insecurity of the network environment and untrust worthiness of the service providers are obstacles of making the Database as a service. Privacy concerns exist wherever personally identifiable information is collected and stored. Public Databases and resources are potential source of risk to user privacy. An intentional Database Owner can guess user details by practically monitoring their queries. Hence the major challenge here is to share the data by protecting personal information. A data retrieval scheme allowing the users to query the Database with out comprpmising the privacy in data item is generally sought. The naive solution for confidentiality is to encrypt data before outsourcing.Query execution,key management and statistical in ference are major challenges in this case. The proposed system suggests a mechanism to store private information and secure retrieval of this data using secret sharing based Secure Multi party Computation(SMC). The idea is to develop a mechanism to store private information with a highly available storage provider which could be accessed from anywhere using queries while hiding the actual data values from the storage provider. Multi party Computation facilitates application of join functions over their private inputs and SMC performs these functions by keeping the input data private. This is achieved by making secret shares of the inputs and manipulating the shares to compute some functions.
Shucheng Yu - One of the best experts on this subject based on the ideXlab platform.
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INFOCOM - Efficient privacy-preserving biometric identification in cloud computing
2013 Proceedings IEEE INFOCOM, 2013Co-Authors: Jiawei Yuan, Shucheng YuAbstract:Biometric identification is a reliable and convenient way of identifying individuals. The widespread adoption of biometric identification requires solid privacy protection against possible misuse, loss, or theft of biometric data. Existing techniques for privacy-preserving biometric identification primarily rely on conventional cryptographic primitives such as homomorphic encryption and oblivious transfer, which inevitably introduce tremendous cost to the system and are not applicable to practical large-scale applications. In this paper, we propose a novel privacy-preserving biometric identification scheme which achieves efficiency by exploiting the power of cloud computing. In our proposed scheme, the biometric Database is encrypted and outsourced to the cloud servers. To perform a biometric identification, the Database Owner generates a credential for the candidate biometric trait and submits it to the cloud. The cloud servers perform identification over the encrypted Database using the credential and return the result to the Owner. During the identification, cloud learns nothing about the original private biometric data. Because the identification operations are securely outsourced to the cloud, the realtime computational/communication costs at the Owner side are minimal. Thorough analysis shows that our proposed scheme is secure and offers a higher level of privacy protection than related solutions such as kNN search in encrypted Databases. Real experiments on Amazon cloud, over Databases of different sizes, show that our computational/communication costs at the Owner side are several magnitudes lower than the existing biometric identification schemes.
Cheng Huang - One of the best experts on this subject based on the ideXlab platform.
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An Efficient and Privacy-Preserving Biometric Identification Scheme in Cloud Computing
IEEE Access, 2018Co-Authors: Liehuang Zhu, Chuan Zhang, Ximeng Liu, Cheng HuangAbstract:Biometric identification has become increasingly popular in recent years. With the development of cloud computing, Database Owners are motivated to outsource the large size of biometric data and identification tasks to the cloud to get rid of the expensive storage and computation costs, which, however, brings potential threats to users’ privacy. In this paper, we propose an efficient and privacy-preserving biometric identification outsourcing scheme. Specifically, the biometric To execute a biometric identification, the Database Owner encrypts the query data and submits it to the cloud. The cloud performs identification operations over the encrypted Database and returns the result to the Database Owner. A thorough security analysis indicates that the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud. Compared with previous protocols, experimental results show that the proposed scheme achieves a better performance in both preparation and identification procedures.
