Privacy-Preserving Ciphertext Multi-Sharing Control for Big Data Storage

Liang, Kaitai; Susilo, Willy; Liu, Joseph K.

doi:10.1109/tifs.2015.2419186

Cited by 81 publications

(2 citation statements)

References 38 publications

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“…This is an important attack because the targeted profile once existed on a social network. A game based model which is also called collusion attack model in proposed in [49]. In this model, the attacker attempts to acquire all encryption keys of data and wins if a valid secret key is acquired.…”

Section: Different Types Of Privacy Attacksmentioning

confidence: 99%

Rise of Big Data – Issues and Challenges

Alabdullah

Beloff

White

2018

2018 21st Saudi Computer Society National Computer Conference (NCC)

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The recent rapid rise in the availability of big data due to Internet-based technologies such as social media platforms and mobile devices has left many market leaders unprepared for handling very large, random and high velocity data. Conventionally, technologies are initially developed and tested in labs and appear to the public through media such as press releases and advertisements. These technologies are then adopted by the general public. In the case of big data technology, fast development and ready acceptance of big data by the user community has left little time to be scrutinized by the academic community. Although many books and electronic media articles are published by professionals and authors for their work on big data, there is still a lack of fundamental work in academic literature. Through survey methods, this paper discusses challenges in different aspects of big data, such as data sources, content format, data staging, data processing, and prevalent data stores. Issues and challenges related to big data, specifically privacy attacks and counter-techniques such as k-anonymity, t-closeness, l-diversity and differential privacy are discussed. Tools and techniques adopted by various organizations to store different types of big data are also highlighted. This study identifies different research areas to address such as a lack of anonymization techniques for unstructured big data, data traffic pattern determination for developing scalable data storage solutions and controlling mechanisms for high velocity data.

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Section: Different Types Of Privacy Attacksmentioning

confidence: 99%

Rise of Big Data – Issues and Challenges

Alabdullah

Beloff

White

2018

2018 21st Saudi Computer Society National Computer Conference (NCC)

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“…The mechanisms have been investigated in this work were, Hadoop security, monitoring, auditing, anonymization, cloud security and key management. Liang, et al [23] introduced a cipher text multi-sharing protocol for guaranteeing the confidentiality of the Big data. This mechanism integrated the benefits of proxy re-encryption and anonymous techniques for secure big data storage service.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Privacy Preserving Approach With Enforcing Policies for Processing Big Data in Spark Framework

Swaminathan¹,

Thangavelu²

2018

IJET

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Ensuring the privacy for the big data stored in a cloud system is one of the demanding and critical process in recent days. Generally, the big data contains a huge amount of data, which requires some security measures and rules for assuring the confidentiality. For this reason, different techniques have been developed in the traditional works, which intends to guarantee the privacy of the big data by implementing key generation, encryption, and anonymization mechanisms. But, it limits the issues of increased time consumption, computational complexity, and error rate. Thus, the proposed work aims to design an enhanced mechanism for a secure big data storage. Here, the user’s bank dataset is considered as the input, which is protected from the unauthorized users by guaranteeing both the privacy and secrecy of the data. Here, the raw dataset is preprocessed to increase the data quality and correctness. Then, the security policies (i.e. rules) are generated for allowing the restricted access on the data by using an Improved FP-Growth (IFP-G) algorithm. Consequently, the sensitive and non-sensitive data attributes are classified based on the extracted features by using an Enhanced Random Forest (ERF) classification technique. At last, the privacy of user’s personal information and other details are protected with the use of a Modified Incognito Anonymization based Privacy Preservation (MIA-PP) algorithm. These enhanced mechanisms guarantee the security and confidentiality of the big data with reduced time consumption and increased accuracy. During experimental evaluation, the results of the proposed privacy mechanism is analyzed and compared by using different measures. Also, some of the existing anonymization and classification techniques have been considered to prove the betterment of the proposed technique.

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A network community restructuring mechanism for transport efficiency improvement in scale‐free complex networks

Cai

Luo

Liu

et al. 2017

Concurrency and Computation

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Recent studies have demonstrated that network community structure can significantly reduce the network transport efficiency. In this paper, the weakening community structure (WCS) strategy based on adding of edges that can effectively weaken the network community characteristics and improve the network transport efficiency is proposed. The WCS performance was validated by experiments, which were performed on pseudo-random network, scale-free artificial network with community structures, and real internet, using the shortest path routing and the local routing. The experimental results have demonstrated that the WCS strategy can greatly improve the network load capacity and reduce the average length of the shortest path by adding a small amount of edges between communities. The proposed mechanism not only provides the improvement of network transport efficiency but also can be adapted for restraining of malicious information propagation through the network. KEYWORDS community structure, malicious information propagation, transport efficiency 1 | INTRODUCTION The scale-free network model introduced by Barabás and Albert, and the small-world network model proposed by Watts and Strogatz have revealed the intrinsic characteristics of network structures, which have greatly promoted the rapid development of complex networks in the last 20 years. Further studies have shown that most of the networks in the real world are inhomogeneous and consist of multiple subnetworks. The connections between internal nodes within a subnetwork are very dense, while the connections between nodes from different subnetworks are very sparse. Such a phenomenon, referred as the community structure in the network, is very common in both artificial and natural networks.Hence, due to the small world model and scale-free characteristics, the community structure has become one of the most common and important attributes of complex network topological structure. 1 Due to the complex networks theory, researchers have attempted to treat a series of complex interconnected systems, such as a social network, Internet, and World Wide Web (WWW), as complex networks that reveal their statistical characteristics and transport patterns, which presents a guidance to the real-world applications. 2 Nowadays, the society is faced with big data and huge network traffics, which are highly dependent on network system efficiency. However, the transport efficiency is greatly reduced in the congestion situations, which might lead to network system collapse and cause big troubles in people daily lives. Therefore, improvement of network transport efficiency and mitigation of congestions have important practical significance.Hitherto, researchers have introduced various network transport strategies, which can be mostly divided into 2 categories. The first category consists of "soft" strategies, wherein the transport efficiency improvement is achieved by better routing mechanisms without changing of network topology. 3-6 On the other hand, the second category is both impac...

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Privacy-Preserving Ciphertext Multi-Sharing Control for Big Data Storage

Cited by 81 publications

References 38 publications

Rise of Big Data – Issues and Challenges

Rise of Big Data – Issues and Challenges

An Enhanced Privacy Preserving Approach With Enforcing Policies for Processing Big Data in Spark Framework

A network community restructuring mechanism for transport efficiency improvement in scale‐free complex networks

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