Valentina Tamanini

Bio: Valentina Tamanini is an academic researcher. The author has contributed to research in topics: Social media & Metadata. The author has an hindex of 1, co-authored 1 publications receiving 52 citations.

Location privacy and public metadata in social media platforms: attitudes, behaviors and opinions

Abstract: The highavailability of geolocation technologies is changing the social media mobile scenario and is exposing users to privacy risks. Different studies have focused on location privacy in the mobile scenario, but the results are conflicting: some say that users are concerned about location privacy, others say they are not. In this paper, we initially investigate attitudes and behaviors of people toward a location-aware scenario; then, we show users the amount of personal and sensitive data that can be extracted from contents publicly available in social platforms, and finally we ask for their opinions about a location-aware scenario. Results show that people who were not initially concerned about privacy are the most worried about the location-aware scenario; conversely, people who were initially concerned are less worried about the location-aware scenario and find the scenario interesting. A deeper analysis of the obtained results allows us to draw guidelines that might be helpful to build an effective location-aware scenario.

Standards, Security and Business Models: Key Challenges for the IoT Scenario

Abstract: The number of physical objects connected to the Internet constantly grows and a common thought says the IoT scenario will change the way we live and work. Since IoT technologies have the potential to be pervasive in almost every aspect of a human life, in this paper, we deeply analyze the IoT scenario. First, we describe IoT in simple terms and then we investigate what current technologies can achieve. Our analysis shows four major issues that may limit the use of IoT (i.e., interoperability, security, privacy, and business models) and it highlights possible solutions to solve these problems. Finally, we provide a simulation analysis that emphasizes issues and suggests practical research directions.

DPMM: dynamic pseudonym-based multiple mix-zones generation for mobile traveler

Abstract: Road traffic information has become indispensable for routine vehicular communication but user location privacy an important issue which did not well addressed. An adversary may attack a user by tracking location in routine vehicular communication. Although, continuously changing pseudonyms is a promising solution to attain location privacy in road networks, it has been observed that changing pseudonym at improper time or location may again become a threat for location preservation. As a result, a number of techniques for pseudonym-change have been proposed to achieve location privacy on road networks but most of location based services depend upon speed, GPS position and direction angle services. Hence, sensitive information is periodically broadcasted in every 100-300 ms providing an opportunity to adversaries for accessing critical information and easily tracking vehicles. Moreover, existing methods such as RPCLP, EPCS and MODP for attaining location privacy in mix-zones environment have severely suffered due to large number of pseudonym-changes. To cope with these issues, we presented a Dynamic Pseudonym based Multiple Mix-zones (DPMM) strategy to acquire the highest level of accuracy and privacy. The concept of executing dynamic pseudonym change has been forwarded with respect to pseudonyms, velocity and direction of moving objects. We performed our simulations by using one SUMO simulator and analyzed results compared with existing pseudonym-changing techniques. Our simulation results outperformed various existing techniques and provided better results for achieving high privacy rate, requiring small number of pseudonym-change as well as providing best performance.

Measuring Externalities: The Imperative Next Step to Sustainability Assessment in Sport

Abstract: A paradox exists between the ways sport organizations evaluate their economic impact, compared with their environmental impact. Although the initial sustainability and corporate social responsibility efforts of sport organizations should be celebrated, it is appropriate to call for the next advancement concerning the assessment and measurement of environmental sustainability efforts in sport organizations. Specifically, there is a need for improved and increased monitoring and measurement of sustainable practices that include negative environmental externalities. To usher this advancement, the authors first reviewed the extant research and current industry practice involving environmental impact reporting in sport. Second, the authors proposed a conceptual framework that expands the scope of environmental assessment to be more comprehensive. As such, this expanded, yet more accurate, assessment of environmental impact can identify specific aspects of the event and the inputs and outputs of the before and after event phases that can be curtailed or modified to reduce environmental impacts of sport events.

Lightweight and Efficient Privacy-Preserving Data Aggregation Approach for the Smart Grid

Abstract: Over the last few years, we have seen the emergence of a wide range of Smart Grid architectures, technologies, and applications made possible by the significant improvements in hardware, software, and networking technologies. One of the challenges that has emerged in the Smart Grid environment is the privacy of Smart Grid users. Although several privacy-preserving techniques have been proposed recently for the Smart Grid environment, many of them suffer from high computation and communication costs, different types of attacks, and the use of complex key management schemes. To address these drawbacks, we propose an efficient, lightweight privacy-preserving data aggregation approach that makes use of symmetric homomorphic encryption and Diffie–Hellman (DH) or Elliptic Curve Diffie–Hellman (ECDH) key exchange methods. In contrast to previously proposed privacy-preserving schemes for the Smart Grid, we demonstrate the superiority of our proposed approach in terms of its low transmission and message overheads, and resiliency against a wide range of session key attacks, and ability to maintain data integrity against unauthorized modification or data forgery and to ensure authenticity of smart meters’ data.

TRank: Ranking Twitter users according to specific topics

Abstract: Twitter is the most popular real-time micro-blogging service and it is a platform where users provide and obtain information at rapid pace. In this scenario, one of the biggest challenge is to find a way to automatically identify the most influential users of a given topic. Currently, there are several approaches that try to address this challenge using different Twitter signals (e.g., number of followers, lists, metadata), but results are not clear and sometimes conflicting. In this paper, we propose TRank, a novel method designed to address the problem of identifying the most influential Twitter users on specific topics identified with hashtags. The novelty of our approach is that it combines different Twitter signals (that represent both the user and the user's tweets) to provide three different indicators that are intended to capture different aspects of being influent. The computation of these indicators is not based on the magnitude of the Twitter signals alone, but they are computed taking into consideration also human factors, as for example the fact that a user with many active followings might have a very noisy time lime and, thus, miss to read many tweets. The experimental assessment confirms that our approach provides results that are more reasonable than the one obtained by mechanisms based on the sole magnitude of data.