User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Scoring users\u27 privacy disclosure across multiple online social networks

Users in online social networking sites unknowingly disclose their sensitive information that aggravate the social and financial risks. Hence, to prevent the information loss and privacy exposure, users need to find ways to quantify their privacy level based on their online social network data. Current studies that focus on measuring the privacy risk and disclosure consider only a single source of data, neglecting the fact that users, in general, can have multiple social network accounts disclosing different sensitive information. In this paper, we investigate an approach that can help social media users to measure their privacy disclosure score (PDS) based on the information shared across multiple social networking sites. In particular, we identify the main factors that have impact on users privacy, namely, sensitivity and visibility, to obtain the final disclosure score for each user. By applying the statistical and fuzzy systems, we can specify the potential information loss for a user by using obtained PDS. Our evaluation results with real social media data show that our method can provide a better estimation of privacy disclosure score for users having presence in multiple online social networks

An automated model to score the privacy of unstructured information—Social media case

One of the common forms of data which is shared by online social media users is free-text formats including comments, posts, blogs and tweets. While users mostly share this unstructured data with their preferred social groups, this textual data may contain sensitive information such as their political or religious views, job details, their opinions and emotions and so on. Hence, sharing this unstructured data can escalate privacy risks and concerns for social media users. Analyses the privacy of unstructured data occurred from textual information comes with difficulties as understanding the calculation metrics are challenging. Although there are various studies on privacy evaluation from the extracted structured information from unstructured data, there are limited privacy scoring methods concentrating on the views of the individuals and cannot satisfy the privacy scoring of shared unstructured data in social networks appropriately. Here, in this paper, we propose an automated fuzzy-based model that can extract the privacy-related features as well as the related shared structured data and measure and warn users regarding the textual data privacy risks they have shared in online social platforms. The proposed model can facilitate mitigation actions for users’ free-format texts shared in various social networks. The evaluation of the study indicates that the proposed model can measure the users’ privacy risk in a more accurate manner compared with previously proposed methods and available commercialised software in the domain

A Privacy-Enhanced Friending Approach for Users on Multiple Online Social Networks

Online social network users share their information in different social sites to establish connections with individuals with whom they want to be a friend. While users share all their information to connect to other individuals, they need to hide the information that can bring about privacy risks for them. As user participation in social networking sites rises, the possibility of sharing information with unknown users increases, and the probability of privacy breaches for the user mounts. This work addresses the challenges of sharing information in a safe manner with unknown individuals. Currently, there are a number of available methods for preserving privacy in order to friending (the act of adding someone as a friend), but they only consider a single source of data and are more focused on users’ security rather than privacy. Consequently, a privacy-preserving friending mechanism should be considered for information shared in multiple online social network sites. In this paper, we propose a new privacy-preserving friending method that helps users decide what to share with other individuals with the reduced risk of being exploited or re-identified. In this regard, the first step is to calculate the sensitivity score for individuals using Bernstein’s polynomial theorem to understand what sort of information can influence a user’s privacy. Next, a new model is applied to anonymise the data of users who participate in multiple social networks. Anonymisation helps to understand to what extent a piece of information can be shared, which allows information sharing with reduced risks in privacy. Evaluation indicates that measuring the sensitivity of information besides anonymisation provides a more accurate outcome for the purpose of friending, in a computationally efficient manner

A Survey on Blockchain-Based Internet Service Architecture: Requirements, Challenges, Trends, and Future

The emergence of Internet protocol suites and packet-switching technologies tend to considerations of security, privacy, scalability, and reliability in layered Internet service architectures. The existing service systems allow us to access big data, but few studies focus on the fundamental security and stability in these systems, especially when they involve large-scale networks with overloaded private information. In this research, we explored the blockchain-based mechanism that aims to improve the critical features of traditional Internet services, including autonomous and decentralized processing, smart contractual enforcement of goals, and traceable trustworthiness in tamper-proof transactions. Furthermore, we provide a comprehensive review to conceptualize the blockchain-based framework to develop decentralized protocols for the extensive number of Internet services. This comprehensive survey aims to address blockchain integration to secure Internet services and identify the critical requirements of developing a decentralized trustworthy Internet service. Additionally, we present a case study of block-chain based IoT for neuroinformatics to illustrate the potential applications of blockchain architectures. Finally, we summarize the trends and challenges of blockchain architectures that benefit a multitude of disciplines across all internet service fields of interest

A blockchain-based framework for automatic SLA management in fog computing environments

Fog computing has become a prominent paradigm in providing shared resources to serve different applications near the edge. Similar to other computing paradigms such as cloud and grid, in fog computing, service-level agreements (SLAs) are essential between fog providers and end-users to guarantee the quality of service (QoS). However, due to the unique characteristics of fog resources, such as being highly distributed and heterogeneous, with their dynamic nature having nonrestrictive provider participation, SLA management techniques and frameworks, which are available for Clouds and Grids, are not directly applicable. The availability of the resources in the cloud is much more controllable and predictable compared to fog. Moreover, due to the multiple ownership of fog infrastructure and unrestricted environment, autonomous end-devices are allowed to participate with different SLAs to serve the applications near the edge as a result is a lack of trust exists between the entities and managing and enforcing SLAs according to the application QoS in this environment is a complex task. Thus, the SLA management must be undertaken in a more trustworthy manner to ensure that agreement. To fill this gap, this paper proposes an automated SLA management framework for fog computing that utilizes Smart contracts and blockchain technology to monitor and enforce SLAs in a more trustworthy manner. The results obtained from the experiments, which were conducted in the blockchain private network, show that the framework can ensure precise and efficient SLAs enforcement in the fog. The performance of the proposed framework is better than existing work in terms of transaction cost and time