Social Network Analysis

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A Break in the Clouds: Towards a Cloud Definition

http://www.parkjonghyuk.net/lecture/2015-1st-lecture/networksecurity/Paper2.pdf

Security in cloud computing

Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.

Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

Cloud computing has emerged as one of the most influential paradigms in the IT industry in recent years. Since this new computing technology requires users to entrust their valuable data to cloud providers, there have been increasing security and privacy concerns on outsourced data. Several schemes employing attribute-based encryption (ABE) have been proposed for access control of outsourced data in cloud computing; however, most of them suffer from inflexibility in implementing complex access control policies. In order to realize scalable, flexible, and fine-grained access control of outsourced data in cloud computing, in this paper, we propose hierarchical attribute-set-based encryption (HASBE) by extending ciphertext-policy attribute-set-based encryption (ASBE) with a hierarchical structure of users. The proposed scheme not only achieves scalability due to its hierarchical structure, but also inherits flexibility and fine-grained access control in supporting compound attributes of ASBE. In addition, HASBE employs multiple value assignments for access expiration time to deal with user revocation more efficiently than existing schemes. We formally prove the security of HASBE based on security of the ciphertext-policy attribute-based encryption (CP-ABE) scheme by Bethencourt and analyze its performance and computational complexity. We implement our scheme and show that it is both efficient and flexible in dealing with access control for outsourced data in cloud computing with comprehensive experiments.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=2383&context=sis_research

HASBE: A Hierarchical Attribute-Based Solution for Flexible and Scalable Access Control in Cloud Computing

Video Surveillance has been widely used in business establishments. Since digital cameras everlastingly collect the video data, the volume of sampled data is extensively large, which is hard to be stored and managed locally. Outsourcing surveillance video data to the cloud can achieve cost saving and flexibility, but also will incur potential privacy leakage. In this paper, we utilize the Compressed Sensing (CS) technique for sampling and compressing, to achieve secure and efficient video surveillance in cloud computing. Firstly, we identify the known-plaintext attack in such environment. That is, given sufficient information about the original signal and corresponding CS measurements, the attacker is likely to calculate the measurement matrix. Then, we propose a Dynamic Compressive Sensing(DCS) scheme to resist such an attack. Specifically, we use a dynamic measurement matrix that is changeable over time to prevent the attackers from gaining sufficient information to calculate the measurement matrix. Furthermore, we allow the cloud to help users decode the non-reference frames without leaking any information, to take full advantage of the powerful computing. Experimental results show that the proposed scheme effectively protects the security of the surveillance video, and provides a good recovery quality for users to conduct further analysis.

Secure and Efficient Video Surveillance in Cloud Computing

The scalable and elastic storage capabilities of cloud computing motivate enterprises and individuals to outsource their data and query services to cloud platforms. Since the cloud service provider (CSP) is outside the trusted domain of cloud users, existing research suggests encrypting data before outsourcing and employing searchable symmetric encryption (SSE) to facilitate keyword-based search on the ciphertexts. To make SSE be more applicable in cloud computing, Kurosawa et al. proposed a dynamic verifiable SSE (DVSSE) scheme, which employed inverted indexes and the RSA accumulator to enable the user to search and update files in a verifiable way. However, their scheme works only under the assumption of an honest but curious CSP. In this paper, we propose a secure DVSSE scheme, \(\hbox {DVSSE}_{S}\), for the untrusted cloud environments. Specifically, \(\hbox {DVSSE}_{S}\) is constructed in two different ways. The basic \(\hbox {DVSSE}_{S}\), called \(\hbox {DVSSE}_{S}\)-1, is constructed based on the Merkle hash tree (MHT) and BLS signatures, which can be easily extended from DVSSE. Since \(\hbox {DVSSE}_{S}\)-1 incurs a heavy cost during the update phase, the advanced \(\hbox {DVSSE}_{S}\), called \(\hbox {DVSSE}_{S}\)-2, utilizes random permutations to improve the performance. Extensive experiments on real data set demonstrate the efficiency and effectiveness of our proposed scheme.

Dynamic Verifiable Search Over Encrypted Data in Untrusted Clouds

Location-Based Services (LBSs) have been facilitating and enriching people’s daily lives. While users enjoy plenty of conveniences, privacy disclosure in terms of both location information and query contents is common. Most of the existing solutions mainly focus on location privacy and adopt K-anonymity principle to preserve user’s privacy. However, these methods are vulnerable to protect user’s query content. In this paper, we propose a Privacy Preserving and Content Protection (PPCP) scheme for LBSs users. Unlike most of researches requiring a trusted third party (TTP), our scheme is based on a semi-trusted middle entity, which is unaware of both the exact location information about issuer and query content in the user’s requirement. We utilize space filling curve to transform user location and protect user query content based on encryption technology, so that the proposed scheme can provide enhanced location privacy and query privacy protection in both snapshot and continuous LBSs.

Privacy Preserving Scheme for Location and Content Protection in Location-Based Services

The popularity of the modern smart devices and mobile social networks (MSNs) brings mobile users better experiences and services by taking advantage of location-aware capabilities. Location sharing, as an important function of MSNs, has attracted attention with growing popularity. While the users get great benefits and conveniences from MSNs, they also have high concerns about the privacy of location. However, in the existing solution, the privacy of users can hardly be guaranteed without the assumption of full trust in the service provider (SP), and few previous research studies have discussed the individual requirement of mobile users in MSNs. In this paper, we propose a user-defined location-sharing scheme (ULSS) to achieve enhanced privacy preservation under different contexts. We present a coarse-grained proximity detection method and a lightweight order-preserving encryption- (OPE-) based method to provide the users with flexible privacy preservation at different privacy levels. The proposed scheme preserves user’s location privacy with respect to SP, friends, and other adversaries, getting rid of the introduction of fully trusted party (TTP). Extensive experiments were conducted to verify the effectiveness and efficiency of our proposed scheme.

/pdf/a-user-defined-location-sharing-scheme-with-efficiency-and-2z540jqllx.pdf

A User-Defined Location-Sharing Scheme with Efficiency and Privacy in Mobile Social Networks

With the increase of location-aware and Internet-capable mobile handset devices, location-based services (LBSs) have experienced an explosive growth in recent years. To scale up services, location-based service providers (LBSPs) outsource data management to third-party cloud service providers (CSPs), which in turn provide data query services to users on behalf of LBSPs. However, the CSPs cannot be trusted, which may return incorrect or incomplete query results to users, intentionally or not. Skyline query is an important kind of query, which asks for the data that is not spatially dominated by any other data. Therefore, enabling users to authenticate skyline query results is essential for outsourced LBSs. In this paper, we propose an authentication solution to support location-based skyline query. By embedding each data with its skyline neighbors in the data’s signature, our solution allows users to efficiently verify the soundness and completeness of location-based skyline query results. Through theoretical analysis, we demonstrate the effectiveness of our proposed solution.

Qin Liu

Papers

Secure and Efficient Video Surveillance in Cloud Computing

Dynamic Verifiable Search Over Encrypted Data in Untrusted Clouds

Privacy Preserving Scheme for Location and Content Protection in Location-Based Services

A User-Defined Location-Sharing Scheme with Efficiency and Privacy in Mobile Social Networks

Authentication of Skyline Query over Road Networks