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

Privacy-Preserving and Outsourced Multi-User k-Means Clustering

Many techniques for privacy-preserving data mining (PPDM) have been investigated over the past decade. Often, the entities involved in the data mining process are end-users or organizations with limited computing and storage resources. As a result, such entities may want to refrain from participating in the PPDM process. To overcome this issue and to take many other benefits of cloud computing, outsourcing PPDM tasks to the cloud environment has recently gained special attention. We consider the scenario where n entities outsource their databases (in encrypted format) to the cloud and ask the cloud to perform the clustering task on their combined data in a privacy-preserving manner. We term such a process as privacy-preserving and outsourced distributed clustering (PPODC). In this paper, we propose a novel and efficient solution to the PPODC problem based on k-means clustering algorithm. The main novelty of our solution lies in avoiding the secure division operations required in computing cluster centers altogether through an efficient transformation technique. Our solution builds the clusters securely in an iterative fashion and returns the final cluster centers to all entities when a pre-determined termination condition holds. The proposed solution protects data confidentiality of all the participating entities under the standard semi-honest model. To the best of our knowledge, ours is the first work to discuss and propose a comprehensive solution to the PPODC problem that incurs negligible cost on the participating entities. We theoretically estimate both the computation and communication costs of the proposed protocol and also demonstrate its practical value through experiments on a real dataset.Comment: 16 pages, 2 figures, 5 table

ESPADE: An Efficient and Semantically Secure Shortest Path Discovery for Outsourced Location-Based Services

With the rapid growth of smart devices and technological advancements in tracking geospatial data, the demand for Location-Based Services (LBS) is facing a constant rise in several domains, including military, healthcare and transportation. It is a natural step to migrate LBS to a cloud environment to achieve on-demand scalability and increased resiliency. Nonetheless, outsourcing sensitive location data to a third-party cloud provider raises a host of privacy concerns as the data owners have reduced visibility and control over the outsourced data. In this paper, we consider outsourced LBS where users want to retrieve map directions without disclosing their location information. Specifically, our paper aims to address the following problem: Given a user’s location s, a target destination t, and a graph G stored in a cloud, can users retrieve the shortest path route from s to t in a privacy-preserving manner? Although there exist a few solutions to this problem, they are either inefficient or insecure. For example, existing solutions either leak intermediate results to untrusted cloud providers or incur significant costs on the end-user. To address this gap, we propose an efficient and secure solution based on homomorphic encryption properties combined with a novel data aggregation technique. We formally show that our solution achieves semantic security guarantees under the semi-honest model. Additionally, we provide complexity analysis and experimental results to demonstrate that the proposed protocol is significantly more efficient than the current state-of-the-art techniques

Secure Multiset Intersection Cardinality and its Application to Jaccard Coefficient

The Jaccard Coefficient, as an information similarity measure, has wide variety of applications, such as cluster analysis and image segmentation. Due to the concerns of personal privacy, the Jaccard Coefficient cannot be computed directly between two independently owned datasets. The problem, secure computation of the Jaccard Coefficient for multisets (SJCM), considers the situation where two parties want to securely compute the random shares of the Jaccard Coefficient between their multisets. During the process, the content of each party\u27s multiset is not disclosed to the other party and also the value of Jaccard Coefficient should be hidden from both parties. Secure computation of multiset intersection cardinality is an important sub-problem of SJCM. Existing methods when applied to solve such a problem can lead to either insecure or inefficient solutions. Our work addresses this gap. We first present a basic SJCM protocol constructed using the existing secure dot product method as a sub-routine. Then, as a major contribution, we propose an approximated version of our basic protocol to improve efficiency without compromising accuracy much. We provide various experimental results to show that the proposed protocols are significantly more efficient than the existing techniques when the domain size is small using both simulated and real datasets

An Efficient and Secure Shortest-Path Discovery in Location-Based Services

Cloud computing is a rapidly growing technology that offers a cost-efficient infrastructure with on-demand scalability and increased flexibility. Security and Privacy has been the biggest hurdle for the widespread cloud migration. Specifically, the data stored in cloud carries a lot of user’s sensitive information and is vulnerable to attacks because of reduced visibility and control. A commonly used data structure in computing is Graph, which is flexible and dynamic. Graphs are ubiquitous and are becoming more pervasive across various domains. One such domain is military, where the graph theory is employed to determine the organization structure and network flow which aids in transporting the supplies for optimal usage of resources while attacking the target. In our project, we focus on Location-Based Services, where users want to retrieve the services like map directions without disclosing their locations. In particular, our research aims to address the following problem: Given a user’s location S and a preferred destination T over a graph G, can we retrieve the shortest path route from S to T in a privacy-preserving manner? Although, there exist some solutions for this problem, they are either inefficient or insecure. To address this gap, we proposed an efficient and secure solution based on homomorphic encryption with novel data aggregation technique. Additionally, we investigate the security and complexity of our solution and present a comparative performance analysis to demonstrate the superiority of the proposed solution compared to the existing work

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

ESPADE: An Efficient and Semantically Secure Shortest Path Discovery for Outsourced Location-Based Services

With the rapid growth of smart devices and technological advancements in tracking geospatial data, the demand for Location-Based Services (LBS) is facing a constant rise in several domains, including military, healthcare and transportation. It is a natural step to migrate LBS to a cloud environment to achieve on-demand scalability and increased resiliency. Nonetheless, outsourcing sensitive location data to a third-party cloud provider raises a host of privacy concerns as the data owners have reduced visibility and control over the outsourced data. In this paper, we consider outsourced LBS where users want to retrieve map directions without disclosing their location information. Specifically, our paper aims to address the following problem: Given a user’s location s, a target destination t, and a graph G stored in a cloud, can users retrieve the shortest path route from s to t in a privacy-preserving manner? Although there exist a few solutions to this problem, they are either inefficient or insecure. For example, existing solutions either leak intermediate results to untrusted cloud providers or incur significant costs on the end-user. To address this gap, we propose an efficient and secure solution based on homomorphic encryption properties combined with a novel data aggregation technique. We formally show that our solution achieves semantic security guarantees under the semi-honest model. Additionally, we provide complexity analysis and experimental results to demonstrate that the proposed protocol is significantly more efficient than the current state-of-the-art techniques

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