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Recommender systems recommend new movies, music, restaurants, etc. Typically, service providers organize such systems in a centralized way, holding all the data. Biases in the recommender systems are not transparent to the user and lock-in effects might make it inconvenient for the user to switch providers. In this paper, we present the concept, design, and implementation of MobRec, a mobile platform that decentralizes the data collection, data storage, and recommendation process. MobRec's architecture does not need any backend and solely consists of the users' smartphones, which already contain the users' preferences and ratings. Being in proximity in public places or public transportation, data is exchanged in a device-to-device manner, building local databases that can recommend new items. One of biggest challenges of such a system is the implementation of unobtrusive device-to-device data exchange on off-the-shelf Android devices and iPhones. MobRec facilitates such data exchange, building on Google Nearby Messages with Bluetooth Low Energy. We achieve the successful exchange of data within 3 to 4 minutes, making it suitable for the described scenario. We demonstrate the feasibility of decentralized recommender systems and provide blueprints for the development of seamless multi-platform device-to-device communication.

Online social networking services (SNSs) have emerged rapidly and have become huge data sources for social network analysis. The spread of the content generated by users is crucial in SNS, but there is only a handful of research works on information diffusion and, more precisely, information diffusion flow. In this paper, we propose a novel method to discover information diffusion processes from SNS data. The method starts preprocessing the SNS data using a user-centric algorithm of community detection based on modularity maximization with the purpose of reducing the complexity of the noisy data. After that, the InfoFlow miner generates information diffusion flow models among the user communities discovered from the data. The algorithm is an extension of a traditional process discovery technique called the Flexible Heuristics miner, but the visualization ability of the generated process model is improved with a new measure called response weight, which effectively captures and represents the interactions among communities. An experiment with Facebook data was conducted, and information flow among user communities was visualized. Additionally, a quality assessment of the models was carried out to demonstrate the effectiveness of the method. The final constructed models allowed us to identify useful information such as how the information flows between communities and information disseminators and receptors within communities.

Mobile social networks (MSNs) are the networks of individuals with similar interests connected to each other through their mobile devices. Recently, MSNs are proliferating fast supported by emerging wireless technologies that allow to achieve more efficient communication and better networking performance across the key parameters, such as lower delay, higher data rate, and better coverage. At the same time, most of the MSN users do not fully recognize the importance of security on their handheld mobile devices. Due to this fact, multiple attacks aimed at capturing personal information and sensitive user data become a growing concern, fueled by the avalanche of new MSN applications and services. Therefore, the goal of this work is to understand whether the contemporary user equipment is susceptible to compromising its sensitive information to the attackers. As an example, various information security algorithms implemented in modern smartphones are thus tested to attempt the extraction of the said private data based on the traces registered with inexpensive contemporary audio cards. Our obtained results indicate that the sampling frequency, which constitutes the strongest limitation of the off-the-shelf side-channel attack equipment, only delivers low-informative traces. However, the success chances to recover sensitive data stored within a mobile device may increase significantly when utilizing more efficient analytical techniques as well as employing more complex attack equipment. Finally, we elaborate on the possible utilization of neural networks to improve the corresponding encrypted data extraction process, while the latter part of this paper outlines solutions and practical recommendations to protect from malicious side-channel attacks and keep the personal user information protected.

Sociologists have long converged that the evolution of a Social Networking Service(SNS) is driven by the interplay between users’ preferences (reflected in user-item interaction behavior) and the social network structure (reflected in user-user interaction behavior). Nevertheless, traditional approaches either modeled these two kinds of behaviors in isolation or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of the dynamic social network structure and users’ historical preferences affect the evolution of SNSs. Furthermore, can transforming the underlying social theories in the platform evolution modeling process benefit both behavior prediction tasks? In this paper, we incorporate the underlying social theories to explain and model the evolution of users’ two kinds of behaviors in SNSs. Specifically, we present two kinds of representations for users’ behaviors: a direct (latent) representation that presumes users’ behaviors are represented directly (latently) by their historical behaviors. Under each representation, we associate each user's two kinds of behaviors with two vectors at each time. Then, for each representation, we propose the corresponding learning model to fuse the interplay between users’ two kinds of behaviors. Finally, extensive experimental results demonstrate the effectiveness of our proposed models for both user preference prediction and social link suggestion. [ABSTRACT FROM PUBLISHER]

Items shared through Social Media may affect more than one user's privacy—e.g., photos that depict multiple users, comments that mention multiple users, events in which multiple users are invited, etc. The lack of multi-party privacy management support in current mainstream Social Media infrastructures makes users unable to appropriately control to whom these items are actually shared or not. Computational mechanisms that are able to merge the privacy preferences of multiple users into a single policy for an item can help solve this problem. However, merging multiple users’ privacy preferences is not an easy task, because privacy preferences may conflict, so methods to resolve conflicts are needed. Moreover, these methods need to consider how users’ would actually reach an agreement about a solution to the conflict in order to propose solutions that can be acceptable by all of the users affected by the item to be shared. Current approaches are either too demanding or only consider fixed ways of aggregating privacy preferences. In this paper, we propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media that is able to adapt to different situations by modelling the concessions that users make to reach a solution to the conflicts. We also present results of a user study in which our proposed mechanism outperformed other existing approaches in terms of how many times each approach matched users’ behaviour. [ABSTRACT FROM PUBLISHER]