Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201

On Security of RASP Data Perturbation for Secure Half-Space Queries in the Cloud

Secure data intensive computing in the cloud is challenging, involving a complicated tradeoff among security, performance, extra costs, and cloud economics. Although fully homomorphic encryption is considered as the ultimate solution, it is still too expensive to be practical at the current stage. In contrast, methods that preserve special types of data utility, even with weaker security, might be acceptable in practice. The recently proposed RASP perturbation method falls into this category. It can provide practical solutions for specific problems such as secure range queries, statistical analysis, and machine learning. The RASP perturbation embeds the multidimensional data into a secret higher dimensional space, enhanced with random noise addition to protect the confidentiality of data. It also provides a query perturbation method to transform half-space queries to a quadratic form and, meanwhile, preserving the results of half-space queries. The utility preserving property and wide application domains are appealing. However, since the security of this method is not thoroughly analyzed, the risk of using this method is unknown. The purpose of this paper is to investigate the security of the RASP perturbation method based on a specific threat model. The threat model defines three levels of adversarial power and the concerned attacks. We show that although the RASP perturbed data and queries are secure on the lowest level of adversarial power, they do not satisfy the strong indistinguishability definition on higher levels of adversarial power. As we have noticed, the indistinguishability definition might not be too strong to be useful in the context of data intensive cloud computation. In addition, the noise component in the perturbation renders it impossible to exactly recover the plain data; thus, all attacks are essentially estimation attacks. We propose a weaker security definition based on information theoretic measures to describe the effectiveness of estimation attacks, and then study the security under this weaker definition. This security analysis helps clearly identify the security weaknesses of the RASP perturbation and quantify the expected security under different levels of adversarial power

Preserving Data Confidentiality and Query Privacy Using KNN-R Approach

Cloud computing is one of the famous and well known technique that processes the data query efficiently. Since it is maintaining huge amount of resources, its privacy and security is an issue. Cloud service providers are not trust worthy, so data is to be secured. Whenever the data is sent to the cloud, it is encrypted because to protect the sensitive data such that query privacy and data confidentiality is assured. Cloud computing reduces the in- house resources .This doesn’t mean processing of the query should be slow. To ensure query privacy and data confidentiality RASP approach is designed. The RASP Perturbation technique combines Order preserving Encryption, Dimensionality Expansion, random noise injection , random projection to provide strong safety to the perturbed data and query. RASP make use of the kNN algorithm to process the query efficiently. kNN approach use the minimum square range to process the query. It transfers data to the multidimensional space where it uses indexing approach to process the minimum square range points. DOI: 10.17762/ijritcc2321-8169.150313

A Novel Approach to Build Reliable and Efficient Query Services in Cloud with RASP Data Perturbation

Inspired and motivated by the widespread development and deployment of public cloud computing infrastructures, using clouds to host data query services has become an fascinating solution for the advantages on scalability and cost-saving. However, Data owners show reluctance to share the sensitive and confidential data unless the Data integrity is guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and securit

Secure and Privacy-Preserving Average Consensus

Average consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision-making, to decentralized control. In order to reach an agreement, existing average consensus algorithms require each agent to exchange explicit state information with its neighbors. This leads to the disclosure of private state information, which is undesirable in cases where privacy is of concern. In this paper, we propose a novel approach that enables secure and privacy-preserving average consensus in a decentralized architecture in the absence of any trusted third-parties. By leveraging homomorphic cryptography, our approach can guarantee consensus to the exact value in a deterministic manner. The proposed approach is light-weight in computation and communication, and applicable to time-varying interaction topology cases. A hardware implementation is presented to demonstrate the capability of our approach.Comment: 7 pages, 4 figures, paper is accepted to CPS-SPC'1

DESIGN AND IMPLEMENTATION OF RASP IN DATA COMMOTION IN BUILDING ARCANE AND CAPABILITY QUERY SERVICES

Because of distinctive advantages in scalability and price-saving, storing of understanding-intensive query services in cloud gets increasingly popular. We setup random space perturbation system to produce realistic range query and k nearest-neighbour services of query in cloud. The forecasted approach will undertake data confidentiality, privacy of query, efficient processing of query furthermore to lessen in-house price of processing, and obtain a great balance within it. Random space perturbation system is a kind of growing perturbation, by mixture of order preserving file encryption, random noise injection, and random project. Random space perturbation system encloses lots of significant features. The fundamental proposal should be to at random modify complex data sets by grouping of order preserving file encryption, random noise injection, random project and dimensionality expansion, to make sure that utility for handing range queries is preserved

Confidential Machine Learning on Untrusted Platforms: a Survey

With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality