scholarly journals Privacy-Preserving Secure Computation of Skyline Query in Distributed Multi-Party Databases

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 119 ◽  
Author(s):  
Mahboob Qaosar ◽  
Asif Zaman ◽  
Md. Siddique ◽  
Annisa ◽  
Yasuhiko Morimoto
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Homomorphic Encryption ◽  
Database Systems ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Sensitive Information ◽  
Computing Environment ◽  
Skyline Query ◽  
Attribute Value

Selecting representative objects from a large-scale database is an essential task to understand the database. A skyline query is one of the popular methods for selecting representative objects. It retrieves a set of non-dominated objects. In this paper, we consider a distributed algorithm for computing skyline, which is efficient enough to handle “big data”. We have noticed the importance of “big data” and want to use it. On the other hand, we must take care of its privacy. In conventional distributed algorithms for computing a skyline query, we must disclose the sensitive values of each object of a private database to another for comparison. Therefore, the privacy of the objects is not preserved. However, such disclosures of sensitive information in conventional distributed database systems are not allowed in the modern privacy-aware computing environment. Recently several privacy-preserving skyline computation frameworks have been introduced. However, most of them use computationally expensive secure comparison protocol for comparing homomorphically encrypted data. In this work, we propose a novel and efficient approach for computing the skyline in a secure multi-party computing environment without disclosing the individual attributes’ value of the objects. We use a secure multi-party sorting protocol that uses the homomorphic encryption in the semi-honest adversary model for transforming each attribute value of the objects without changing their order on each attribute. To compute skyline we use the order of the objects on each attribute for comparing the dominance relationship among the objects. The security analysis confirms that the proposed framework can achieve multi-party skyline computation without leaking the sensitive attribute value to others. Besides that, our experimental results also validate the effectiveness and scalability of the proposed privacy-preserving skyline computation framework.

Privacy-preserving policy evaluation in multi-party access control

2021 ◽  
pp. 1-38
Author(s):  
Mina Sheikhalishahi ◽  
Ischa Stork ◽  
Nicola Zannone
Keyword(s):  
Access Control ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Sensitive Information ◽  
Functional Evaluation ◽  
Collaborative Systems ◽  
New Paradigm ◽  
Access Control Policies ◽  
The Individual

Recent years have seen an increasing popularity of online collaborative systems like social networks and web-based collaboration platforms. Collaborative systems typically offer their users a digital environment in which they can work together and share resources and information. These resources and information might be sensitive and, thus, they should be protected from unauthorized accesses. Multi-party access control is emerging as a new paradigm for the protection of co-owned and co-managed resources, where the policies of all users involved in the management of a resource should be accounted for collaborative decision making. Existing approaches, however, only focus on the jointly protection of resources and do not address the protection of the individual user policies themselves, whose disclosure might leak sensitive information. In this work, we propose a privacy-preserving mechanism for the evaluation of multi-party access control policies, which preserves the confidentiality of user policies while remaining capable of making collaborative decisions. To this end, we design secure computation protocols for the evaluation of policies in protected form against an access query and realize such protocols using two privacy-preserving techniques, namely Homomorphic Encryption and Secure Functional Evaluation. We show the practical feasibility of our mechanism in terms of computation and communication costs through an experimental evaluation.

scholarly journals Efficient and Privacy-Preserving Multi-User Outsourced K-Means Clustering

2021 ◽  
Vol 14 (2) ◽  
pp. 26
Author(s):  
Na Li ◽  
Lianguan Huang ◽  
Yanling Li ◽  
Meng Sun
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Locality Sensitive Hashing ◽  
Sensitive Information ◽  
The Public ◽  
Big Data Mining ◽  
Euclidean Distances ◽  
Computational Resources

In recent years, with the development of the Internet, the data on the network presents an outbreak trend. Big data mining aims at obtaining useful information through data processing, such as clustering, clarifying and so on. Clustering is an important branch of big data mining and it is popular because of its simplicity. A new trend for clients who lack of storage and computational resources is to outsource the data and clustering task to the public cloud platforms. However, as datasets used for clustering may contain some sensitive information (e.g., identity information, health information), simply outsourcing them to the cloud platforms can't protect the privacy. So clients tend to encrypt their databases before uploading to the cloud for clustering. In this paper, we focus on privacy protection and efficiency promotion with respect to k-means clustering, and we propose a new privacy-preserving multi-user outsourced k-means clustering algorithm which is based on locality sensitive hashing (LSH). In this algorithm, we use a Paillier cryptosystem encrypting databases, and combine LSH to prune off some unnecessary computations during the clustering. That is, we don't need to compute the Euclidean distances between each data record and each clustering center. Finally, the theoretical and experimental results show that our algorithm is more efficient than most existing privacy-preserving k-means clustering.

scholarly journals An Efficient Identity-Based Conditional Privacy-Preserving Authentication Scheme for Secure Communication in a Vehicular Ad Hoc Network

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1687 ◽  
Author(s):  
Mahmood A. Al-shareeda ◽  
Mohammed Anbar ◽  
Selvakumar Manickam ◽  
Iznan H. Hasbullah
Keyword(s):  
Secure Communication ◽  
Large Scale ◽  
Ad Hoc ◽  
Privacy Preserving ◽  
Authentication Scheme ◽  
Security And Privacy ◽  
Group Signature ◽  
Side Channel ◽  
Sensitive Information ◽  
Identity Based

The security and privacy issues in vehicular ad hoc networks (VANETs) are often addressed with schemes based on either public key infrastructure, group signature, or identity. However, none of these schemes appropriately address the efficient verification of multiple VANET messages in high-density traffic areas. Attackers could obtain sensitive information kept in a tamper-proof device (TPD) by using a side-channel attack. In this paper, we propose an identity-based conditional privacy-preserving authentication scheme that supports a batch verification process for the simultaneous verification of multiple messages by each node. Furthermore, to thwart side-channel attacks, vehicle information in the TPD is periodically and frequently updated. Finally, since the proposed scheme does not utilize the bilinear pairing operation or the Map-To-Point hash function, its performance outperforms other schemes, making it viable for large-scale VANETs deployment.

scholarly journals A Framework for Privacy-Preserving Multi-Party Skyline Query Based on Homomorphic Encryption

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 167481-167496 ◽  
Author(s):  
Mahboob Qaosar ◽  
Kazi Md. Rokibul Alam ◽  
Asif Zaman ◽  
Chen Li ◽  
Saleh Ahmed ◽  
...  
Keyword(s):  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Skyline Query

Optimal Privacy Preserving Technique Over Big Data Analytics Using Oppositional Fruit Fly Algorithm

2020 ◽  
Vol 13 (2) ◽  
pp. 283-295
Author(s):  
Ajmeera Kiran ◽  
Vasumathi Devara
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Input Data ◽  
Clustering Algorithm ◽  
Big Data Analytics ◽  
Fruit Fly ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Convolution Process ◽  
Fuzzy C Means Clustering

Background: Big data analytics is the process of utilizing a collection of data accompanied on the internet to store and retrieve anywhere and at any time. Big data is not simply a data but it involves the data generated by variety of gadgets or devices or applications. Objective: When massive volume of data is stored, there is a possibility for malevolent attacks on the searching data are stored in the server because of under privileged privacy preserving approaches. These traditional methods result in many drawbacks due to various attacks on sensitive information. Hence, to enhance the privacy preserving for sensitive information stored in the database, the proposed method makes use of efficient methods. Methods: In this manuscript, an optimal privacy preserving over big data using Hadoop and mapreduce framework is proposed. Initially, the input data is grouped by modified fuzzy c means clustering algorithm. Then we are performing a map reduce framework. And then the clustered data is fed to the mapper; in mapper the privacy of input data is done by convolution process. To validate the privacy of input data the recommended technique utilizes the optimal artificial neural network. Here, oppositional fruit fly algorithm is used to enhancing the neural networks. Results: The routine of the suggested system is assessed by means of clustering accuracy, error value, memory, and time. The experimentation is performed by KDD dataset. Conclusion: A result shows that our proposed system has maximum accuracy and attains the effective convolution process to improve privacy preserving.

scholarly journals Automating Open Science for Big Data

2015 ◽  
Vol 659 (1) ◽  
pp. 260-273 ◽  
Author(s):  
Mercè Crosas ◽  
Gary King ◽  
James Honaker ◽  
Latanya Sweeney
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Science Research ◽  
Science Research ◽  
Open Science ◽  
Streaming Data ◽  
Sensitive Information ◽  
Increased Risk ◽  
The Social ◽  
New Challenges

The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher’s computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. The trend toward big data—including large-scale streaming data—is starting to transform research and has the potential to impact policymaking as well as our understanding of the social, economic, and political problems that affect human societies. However, big data research poses new challenges to the execution of the analysis, archiving and reuse of the data, and reproduction of the results. Downloading these datasets to a researcher’s computer is impractical, leading to analyses taking place in the cloud, and requiring unusual expertise, collaboration, and tool development. The increased amount of information in these large datasets is an advantage, but at the same time it poses an increased risk of revealing personally identifiable sensitive information. In this article, we discuss solutions to these new challenges so that the social sciences can realize the potential of big data.

Privacy-Preserving Auction for Big Data Trading Using Homomorphic Encryption

2020 ◽  
Vol 7 (2) ◽  
pp. 776-791 ◽  
Author(s):  
Weichao Gao ◽  
Wei Yu ◽  
Fan Liang ◽  
William G. Hatcher ◽  
Chao Lu
Keyword(s):  
Big Data ◽  
Homomorphic Encryption ◽  
Privacy Preserving

scholarly journals Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features

2021 ◽  
Vol 2021 (2) ◽  
pp. 5-26
Author(s):  
Takao Murakami ◽  
Koki Hamada ◽  
Yusuke Kawamoto ◽  
Takuma Hatano
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Tensor Factorization ◽  
Statistical Features ◽  
User Privacy ◽  
Posterior Sampling ◽  
Spatial Big Data

Abstract With the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting user privacy. In particular, a synthetic trace that preserves a feature specific to a cluster of users (e.g., those who commute by train, those who go shopping) is important for various geo-data analysis tasks and for providing a synthetic location dataset. Although location synthesizers have been widely studied, existing synthesizers do not provide su˚cient utility, privacy, or scalability, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer called PPMTF (Privacy-Preserving Multiple Tensor Factorization). We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We factorize these two tensors simultaneously via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors, and perform a plausible deniability test for a synthetic trace. We comprehensively evaluate PPMTF using two datasets. Our experimental results show that PPMTF preserves various statistical features including cluster-specific features, protects user privacy, and synthesizes large-scale location traces in practical time. PPMTF also significantly outperforms the state-of-theart methods in terms of utility and scalability at the same level of privacy.

scholarly journals MCEEP-BDA: Multilevel Clustering Based -Energy Efficient Privacy-Preserving Big Data Aggregation in Large-Scale Wsn

2019 ◽  
Vol 9 (1) ◽  
pp. 6779-6785
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Large Scale ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Communication Overhead

In current scenario, the Big Data processing that includes data storage, aggregation, transmission and evaluation has attained more attraction from researchers, since there is an enormous data produced by the sensing nodes of large-scale Wireless Sensor Networks (WSNs). Concerning the energy efficiency and the privacy conservation needs of WSNs in big data aggregation and processing, this paper develops a novel model called Multilevel Clustering based- Energy Efficient Privacy-preserving Big Data Aggregation (MCEEP-BDA). Initially, based on the pre-defined structure of gradient topology, the sensor nodes are framed into clusters. Further, the sensed information collected from each sensor node is altered with respect to the privacy preserving model obtained from their corresponding sinks. The Energy model has been defined for determining the efficient energy consumption in the overall process of big data aggregation in WSN. Moreover, Cluster_head Rotation process has been incorporated for effectively reducing the communication overhead and computational cost. Additionally, algorithm has been framed for Least BDA Tree for aggregating the big sensor data through the selected cluster heads effectively. The simulation results show that the developed MCEEP-BDA model is more scalable and energy efficient. And, it shows that the Big Data Aggregation (BDA) has been performed here with reduced resource utilization and secure manner by the privacy preserving model, further satisfying the security concerns of the developing application-oriented needs.

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