Privacy-preserving query processing over encrypted data in cloud

The query processing of relational data has been studied extensively throughout the past decade. A number of theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, data owners now have the opportunity to outsource not only their data but also data processing functionalities to the cloud. Because of data security and personal privacy concerns, sensitive data (e.g., medical records) should be encrypted before being outsourced to a cloud, and the cloud should perform query processing tasks on the encrypted data only. These tasks are termed as Privacy-Preserving Query Processing (PPQP) over encrypted data. Based on the concept of Secure Multiparty Computation (SMC), SMC-based distributed protocols were developed to allow the cloud to perform queries directly over encrypted data. These protocols protect the confidentiality of the stored data, user queries, and data access patterns from cloud service providers and other unauthorized users. Several queries were considered in an attempt to create a well-defined scope. These queries included the k-Nearest Neighbor (kNN) query, advanced analytical query, and correlated range query. The proposed protocols utilize an additive homomorphic cryptosystem and/or a garbled circuit technique at different stages of query processing to achieve the best performance. In addition, by adopting a multi-cloud computing paradigm, all computations can be done on the encrypted data without using very expensive fully homomorphic encryptions. The proposed protocols\u27 security was analyzed theoretically, and its practicality was evaluated through extensive empirical results --Abstract, page iii

Secure k-Nearest Neighbor Query over Encrypted Data in Outsourced Environments

For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by the cloud; otherwise, there would be no point to outsource the data at the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first present a basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns. Also, we empirically analyze the efficiency of our protocols through various experiments. These results indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query.Comment: 23 pages, 8 figures, and 4 table

k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text overlap with arXiv:1307.482

Secure Similar Sequence Query on Outsourced Genomic Data

The growing availability of genomic data is unlocking research potentials on genomic-data analysis. It is of great importance to outsource the genomic-analysis tasks onto clouds to leverage their powerful computational resources over the large-scale genomic sequences. However, the remote placement of the data raises personal-privacy concerns, and it is challenging to evaluate data-analysis functions on outsourced genomic data securely and efficiently. In this work, we study the secure similar-sequence-query (SSQ) problem over outsourced genomic data, which has not been fully investigated. To address the challenges of security and efficiency, we propose two protocols in the mixed form, which combine two-party secure secret sharing, garbled circuit, and partial homomorphic encryptions together and use them to jointly fulfill the secure SSQ function. In addition, our protocols support multi-user queries over a joint genomic data set collected from multiple data owners, making our solution scalable. We formally prove the security of protocols under the semi-honest adversary model, and theoretically analyze the performance. We use extensive experiments over real-world dataset on a commercial cloud platform to validate the efficacy of our proposed solution, and demonstrate the performance improvements compared with state-of-the-art works

Secure k-nearest neighbor query over encrypted data in outsourced environments,” eprint arXiv:1307.4824,

Abstract-For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by the cloud; otherwise, there would be no point to outsource the data at the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first present a basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns. Also, we empirically analyze the efficiency of our protocols through various experiments. These results indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query

A Secure Data Sharing and Query Processing Framework Via Federation of Cloud Computing

Due to cost-efficiency and less hands-on management, data owners are outsourcing their data to the cloud which can provide access to the data as a service. However, by outsourcing their data to the cloud, the data owners lose control over their data as the cloud provider becomes a third party service provider. At first, encrypting the data by the owner and then exporting it to the cloud seems to be a good approach. However, there is a potential efficiency problem with the outsourced encrypted data when the data owner revokes some of the users\u27 access privileges. An existing solution to this problem is based on symmetric key encryption scheme but it is not secure when a revoked user rejoins the system with different access privileges to the same data record. In this paper, we propose an efficient and Secure Data Sharing (SDS) framework using homomorphic encryption and proxy re-encryption schemes that prevents the leakage of unauthorized data when a revoked user rejoins the system. We also modify our underlying SDS framework and present a new solution based on the data distribution technique to prevent the information leakage in the case of collusion between a revoked user and the cloud service provider. A comparison of the proposed solution with existing methods is provided in detail. Furthermore, we demonstrate how the existing work can be utilized in our proposed framework to support secure query processing. We provide a detailed security as well as experimental analysis of the proposed framework on Amazon EC2 and highlight its practical value

An Efficient and Secure Data Sharing Framework using Homomorphic Encryption in the Cloud

Due to cost-efficiency and less hands-on management, data owners are outsourcing their data to the cloud which can provide access to the data as a service. However, by outsourcing their data to the cloud, the data owners lose control over their data as the cloud provider becomes a third party. At first, encrypting the data by the owner and then exporting it to the cloud seems to be a good approach. However, there is a potential efficiency problem with the outsourced encrypted data when the data owner revokes some of the users\u27 access privileges. An existing solution to this problem is based on symmetric key encryption scheme and so it is not secure when a revoked user rejoins the system with different access privileges to the same data record. In this paper, we propose an efficient and Secure Data Sharing (SDS) framework using homomorphic encryption and proxy re-encryption schemes that prevents the leakage of unauthorized data when a revoked user rejoins the system. Our framework is secure under the security definition of Secure Multi- Party Computation (SMC) and also is a generic approach - any additive homomorphic encryption and proxy re-encryption schemes can be used as the underlying sub-routines. In addition, we also modify our underlying Secure Data Sharing (SDS) framework and present a new solution based on the data distribution technique to prevent the information leakage in the case of collusion between a user and the Cloud Service Provider

A Secure Data Sharing and Query Processing Framework via Federation of Cloud Computing

Due to cost-efficiency and less hands-on management, data owners are outsourcing their data to the cloud which can provide access to the data as a service. However, by outsourcing their data to the cloud, the data owners lose control over their data as the cloud provider becomes a third party service provider. At first, encrypting the data by the owner and then exporting it to the cloud seems to be a good approach. However, there is a potential efficiency problem with the outsourced encrypted data when the data owner revokes some of the users\u27 access privileges. An existing solution to this problem is based on symmetric key encryption scheme but it is not secure when a revoked user rejoins the system with different access privileges to the same data record. In this paper, we propose an efficient and Secure Data Sharing (SDS) framework using homomorphic encryption and proxy re-encryption schemes that prevents the leakage of unauthorized data when a revoked user rejoins the system. We also modify our underlying SDS framework and present a new solution based on the data distribution technique to prevent the information leakage in the case of collusion between a revoked user and the cloud service provider. A comparison of the proposed solution with existing methods is provided in detail. Furthermore, we demonstrate how the existing work can be utilized in our proposed framework to support secure query processing. We provide a detailed security as well as experimental analysis of the proposed framework on Amazon EC2 and highlight its practical value