scholarly journals PMDP: A Framework for Preserving Multiparty Data Privacy in Cloud Computing

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Ji Li ◽  
Jianghong Wei ◽  
Wenfen Liu ◽  
Xuexian Hu
Keyword(s):  
Cloud Computing ◽  
Data Privacy ◽  
Differential Privacy ◽  
Security Analysis ◽  
Cost Effective ◽  
Original Data ◽  
User Privacy ◽  
Practical Applications ◽  
Cloud Servers ◽  
Privacy Issues

The amount of Internet data is significantly increasing due to the development of network technology, inducing the appearance of big data. Experiments have shown that deep mining and analysis on large datasets would introduce great benefits. Although cloud computing supports data analysis in an outsourced and cost-effective way, it brings serious privacy issues when sending the original data to cloud servers. Meanwhile, the returned analysis result suffers from malicious inference attacks and also discloses user privacy. In this paper, to conquer the above privacy issues, we propose a general framework for Preserving Multiparty Data Privacy (PMDP for short) in cloud computing. The PMDP framework can protect numeric data computing and publishing with the assistance of untrusted cloud servers and achieve delegation of storage simultaneously. Our framework is built upon several cryptography primitives (e.g., secure multiparty computation) and differential privacy mechanism, which guarantees its security against semihonest participants without collusion. We further instantiate PMDP with specific algorithms and demonstrate its security, efficiency, and advantages by presenting security analysis and performance discussion. Moreover, we propose a security enhanced framework sPMDP to resist malicious inside participants and outside adversaries. We illustrate that both PMDP and sPMDP are reliable and scale well and thus are desirable for practical applications.

scholarly journals A Secure Ciphertext Retrieval Scheme against Insider KGAs for Mobile Devices in Cloud Storage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Run Xie ◽  
Chanlian He ◽  
Dongqing Xie ◽  
Chongzhi Gao ◽  
Xiaojun Zhang
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Privacy ◽  
Security Analysis ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Security Issues ◽  
Efficiency Comparison ◽  
Cloud Servers

With the advent of cloud computing, data privacy has become one of critical security issues and attracted much attention as more and more mobile devices are relying on the services in cloud. To protect data privacy, users usually encrypt their sensitive data before uploading to cloud servers, which renders the data utilization to be difficult. The ciphertext retrieval is able to realize utilization over encrypted data and searchable public key encryption is an effective way in the construction of encrypted data retrieval. However, the previous related works have not paid much attention to the design of ciphertext retrieval schemes that are secure against inside keyword-guessing attacks (KGAs). In this paper, we first construct a new architecture to resist inside KGAs. Moreover we present an efficient ciphertext retrieval instance with a designated tester (dCRKS) based on the architecture. This instance is secure under the inside KGAs. Finally, security analysis and efficiency comparison show that the proposal is effective for the retrieval of encrypted data in cloud computing.

scholarly journals The Challenges of Institutional Distance: Data Privacy Issues in Cloud Computing

2018 ◽  
Vol 24 (1) ◽  
pp. 161-181 ◽  
Author(s):  
Yashar Abed ◽  
Meena Chavan
Keyword(s):  
Cloud Computing ◽  
Multinational Corporations ◽  
Data Privacy ◽  
Service Providers ◽  
Host Countries ◽  
Case Study Methodology ◽  
Regulatory Constraints ◽  
Cloud Computing Service ◽  
Privacy Issues

Data protection and data privacy are significant challenges in cloud computing for multinational corporations. There are no standard laws to protect data across borders. The institutional and regulatory constraints and governance differ across countries. This article explores the challenges of institutional constraints faced by cloud computing service providers in regard to data privacy issues across borders. Through a qualitative case study methodology, this research compares the institutional structure of a few host countries, with regard to data privacy in cloud computing and delineates a relative case study. This article will also review the cloud computing legal frameworks and the history of cloud computing to make the concept more comprehensible to a layman.

scholarly journals Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hua Dai ◽  
Hui Ren ◽  
Zhiye Chen ◽  
Geng Yang ◽  
Xun Yi
Keyword(s):  
Data Privacy ◽  
Logistic Map ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Service Recommendation ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Sorting Algorithms ◽  
Common Operation ◽  
Cloud Servers

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

scholarly journals Efficient Multifactor Two-Server Authenticated Scheme under Mobile Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ziyi Han ◽  
Li Yang ◽  
Shen Wang ◽  
Sen Mu ◽  
Qiang Liu
Keyword(s):  
Cloud Computing ◽  
Information Disclosure ◽  
Mobile Cloud Computing ◽  
User Authentication ◽  
High Reliability ◽  
Security Analysis ◽  
Security Requirements ◽  
Mobile Payment ◽  
Mobile Cloud ◽  
User Privacy

Because the authentication method based on username-password has the disadvantage of easy disclosure and low reliability and the excess password management degrades the user experience tremendously, the user is eager to get rid of the bond of the password in order to seek a new way of authentication. Therefore, the multifactor biometrics-based user authentication wins the favor of people with advantages of simplicity, convenience, and high reliability. Now the biometrics-based (especially the fingerprint information) authentication technology has been extremely mature, and it is universally applied in the scenario of the mobile payment. Unfortunately, in the existing scheme, biometric information is stored on the server side. As thus, once the server is hacked by attackers to cause the leakage of the fingerprint information, it will take a deadly threat to the user privacy. Aiming at the security problem due to the fingerprint information in the mobile payment environment, we propose a novel multifactor two-server authenticated scheme under mobile cloud computing (MTSAS). In the MTSAS, it divides the authentication method and authentication means; in the meanwhile, the user’s biometric characteristics cannot leave the user device. Thus, MTSAS avoids the fingerprint information disclosure, protects user privacy, and improves the security of the user data. In the same time, considering user actual requirements, different authentication factors depending on the privacy level of authentication are chosen. Security analysis proves that MTSAS has achieved the authentication purpose and met security requirements by the BAN logic. In comparison with other schemes, the result shows that MTSAS not only has the reasonable computational efficiency, but also keeps the superior communication cost.

scholarly journals A Secure Authentication and Key Agreement Scheme for IoT-Based Cloud Computing Environment

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 150 ◽  
Author(s):  
Yicheng Yu ◽  
Liang Hu ◽  
Jianfeng Chu
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Secure Communication ◽  
Key Agreement ◽  
Security Analysis ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Authentication And Key Agreement ◽  
Insider Attack ◽  
Cloud Servers

The integration of Internet of things (IoT) and cloud computing technology has made our life more convenient in recent years. Cooperating with cloud computing, Internet of things can provide more efficient and practical services. People can accept IoT services via cloud servers anytime and anywhere in the IoT-based cloud computing environment. However, plenty of possible network attacks threaten the security of users and cloud servers. To implement effective access control and secure communication in the IoT-based cloud computing environment, identity authentication is essential. In 2016, He et al. put forward an anonymous authentication scheme, which is based on asymmetric cryptography. It is claimed that their scheme is capable of withstanding all kinds of known attacks and has good performance. However, their scheme has serious security weaknesses according to our cryptanalysis. The scheme is vulnerable to insider attack and DoS attack. For overcoming these weaknesses, we present an improved authentication and key agreement scheme for IoT-based cloud computing environment. The automated security verification (ProVerif), BAN-logic verification, and informal security analysis were performed. The results show that our proposed scheme is secure and can effectively resist all kinds of known attacks. Furthermore, compared with the original scheme in terms of security features and performance, our proposed scheme is feasible.

scholarly journals Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jinbo Xiong ◽  
Rong Ma ◽  
Lei Chen ◽  
Youliang Tian ◽  
Li Lin ◽  
...  
Keyword(s):  
Data Privacy ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Security Analysis ◽  
Mobile Crowdsensing ◽  
Sensing Data ◽  
Noise Data ◽  
And Performance ◽  
Further Development ◽  
The Internet Of Things

Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users’ data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user’s privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners’ dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

scholarly journals Ciphertext-Policy Attribute-Based Encryption for Cloud Storage: Toward Data Privacy and Authentication in AI-Enabled IoT System

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 68
Author(s):  
P. Chinnasamy ◽  
P. Deepalakshmi ◽  
Ashit Kumar Dutta ◽  
Jinsang You ◽  
Gyanendra Prasad Joshi
Keyword(s):  
Data Privacy ◽  
Partial Information ◽  
User Privacy ◽  
Access Policy ◽  
Insider Attack ◽  
Attribute Based Encryption ◽  
Encryption Schemes ◽  
A New Technique ◽  
Ciphertext Policy ◽  
Cloud Servers

People can store their data on servers in cloud computing and allow public users to access data via data centers. One of the most difficult tasks is to provide security for the access policy of data, which is also needed to be stored at cloud servers. The access structure (policy) itself may reveal partial information about what the ciphertext contains. To provide security for the access policy of data, a number of encryption schemes are available. Among these, CP-ABE (Ciphertext-Policy Attribute-Based Encryption) scheme is very significant because it helps to protect, broadcast, and control the access of information. The access policy that is sent as plaintext in the existing CP-ABE scheme along with a ciphertext may leak user privacy and data privacy. To resolve this problem, we hereby introduce a new technique, which hides the access policy using a hashing algorithm and provides security against insider attack using a signature verification scheme. The proposed system is compared with existing CP-ABE schemes in terms of computation and expressive policies. In addition, we can test the functioning of any access control that could be implemented in the Internet of Things (IoT). Additionally, security against indistinguishable adaptive chosen ciphertext attacks is also analyzed for the proposed work.

scholarly journals PPK-Means: Achieving Privacy-Preserving Clustering Over Encrypted Multi-Dimensional Cloud Data

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 310 ◽  
Author(s):  
Hui Yin ◽  
Jixin Zhang ◽  
Yinqiao Xiong ◽  
Xiaofeng Huang ◽  
Tiantian Deng
Keyword(s):  
Cloud Computing ◽  
Data Clustering ◽  
Data Privacy ◽  
Real Life ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Data Sets ◽  
Cloud Data ◽  
Practical Applications ◽  
Computing Paradigm

Clustering is a fundamental and critical data mining branch that has been widely used in practical applications such as user purchase model analysis, image color segmentation, outlier detection, and so on. With the increasing popularity of cloud computing, more and more encrypted data are converging to cloud computing platforms for enjoying the revolutionary advantages of the cloud computing paradigm, as well as mitigating the deeply concerned data privacy issues. However, traditional data encryption makes existing clustering schemes no more effective, which greatly obstructs effective data utilization and frustrates the wide adoption of cloud computing. In this paper, we focus on solving the clustering problem over encrypted cloud data. In particular, we propose a privacy-preserving k-means clustering technology over encrypted multi-dimensional cloud data by leveraging the scalar-product-preserving encryption primitive, called PPK-means. The proposed technique is able to achieve efficient multi-dimensional data clustering as well to preserve the confidentiality of the outsourced cloud data. To the best of our knowledge, our work is the first to explore the privacy-preserving multi-dimensional data clustering in the cloud computing environment. Extensive experiments in simulation data-sets and real-life data-sets demonstrate that our proposed PPK-means is secure, efficient, and practical.

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