At the heart of Blockchains is the trustless leader election mechanism for achieving consensus among pseudo-anonymous peers, without the need of oversight from any third party or authority whatsoever. So far, two main mechanisms are being discussed: proof-of-work (PoW) and proof-of-stake (PoS). PoW relies on demonstration of computational power, and comes with the markup of huge energy wastage in return of the stake in cyrpto-currency. PoS tries to address this by relying on owned stake (i.e., amount of crypto-currency) in the system. In both cases, Blockchains are limited to systems with financial basis. This forces non-crypto-currency Blockchain applications to resort to "permissioned" setting only, effectively centralizing the system. However, non-crypto-currency permisionless blockhains could enable secure and self-governed peer-to-peer structures for numerous emerging application domains, such as education and health, where some trust exists among peers. This creates a new possibility for valuing trust among peers and capitalizing it as the basis (stake) for reaching consensus. In this paper we show that there is a viable way for permisionless non-financial Blockhains to operate in completely decentralized environments and achieve leader election through proof-of-trust (PoT). In our PoT construction, peer trust is extracted from a trust network that emerges in a decentralized manner and is used as a waiver for the effort to be spent for PoW, thus dramatically reducing total energy expenditure of the system. Furthermore, our PoT construction is resilient to the risk of small cartels monopolizing the network (as it happens with the mining-pool phenomena in PoW) and is not vulnerable to sybils. We evluate security guarantees, and perform experimental evaluation of our construction, demonstrating up to 10-fold energy savings compared to PoW without trading off any of the decentralization characteristics, with further guarantees against risks of monopolization.

Data sharing and access control management is one of the issues still hindering the development of decentralized online social networks (DOSNs), which are now gaining more research attention with the recent developments in P2P computing, such as the secure public ledger–based protocols (Blockchains) for monetary systems. In a previous work, we proposed an initial audit–based model for access control in DOSNs. In this article, we focus on enhancing the audit strategies and the privacy issues emerging from records kept for audit purposes. We propose enhanced audit and collaboration strategies, for which experimental results, on a real online social network graph with simulated sharing behavior, show an improvement in the detection rate of bad behavior of more than 50% compared to the basic model. We also provide an analysis of the related privacy issues and discuss possible privacy-preserving alternatives.

The web service computing paradigm has introduced great benefits to the growth of e-markets, both under the customer to business and the business to business models. The value capabilities allowed by the conception of web services, such as interoperability, efficiency, just-in-time integration, etc., have made them the most common way of doing business online. With the maturation of the web services underlying functional properties and facilitating standards, and with the proliferation of the amounts of data they use and they generate, researchers and practitioners have been dedicating considerable efforts to the related emerging privacy concerns. The literature contains number of research works on these privacy concerns, each addressing them from a different focal point. We have explored the available literature on web services privacy during transactions, to present, in this paper, a thorough survey of the most relevant published proposals. We identified 20 works that address privacy related problems in web services consumption. We categorize them based on the approach they take and we compare them based on a proposed evaluation framework, derived from the adopted techniques and addressed requirements.

This tutorial provides a thorough review of the main research directions in the field of identity management and identity related security threats in Online Social Networks (OSNs). The continuous increase in the numbers and sophistication levels of fake accounts constitutes a big threat to the privacy and to the security of honest OSN users. Uninformed OSN users could be easily fooled into accepting friendship links with fake accounts, giving them by that access to personal information they intend to exclusively share with their real friends. Moreover, these fake accounts subvert the security of the system by spreading malware, connecting with honest users for nefarious goals such as sexual harassment or child abuse, and make the social computing environment mostly untrustworthy. The tutorial introduces the main available research results available in this area, and presents our work on collaborative identity validation techniques to estimate OSN profiles trustworthiness.

Fake accounts in online social networks (OSNs) have known considerable sophistication and are now attempting to gain network trust by infiltrating within honest communities. Honest users have limited perspective on the truthfulness of new online identities requesting their friendship. This facilitates the task of fake accounts in deceiving honest users to befriend them. To address this, we have proposed a model that learns hidden correlations between profile attributes within OSN communities, and exploits them to assist users in estimating the trustworthiness of new profiles. To demonstrate our method, we suggest, in this demo, a game application through which players try to cheat the system and convince nodes in a simulated OSN to befriend them. The game deploys different strategies to challenge the players and to reach the objectives of the demo. These objectives are to make participants aware of how fake accounts can infiltrate within their OSN communities, to demonstrate how our suggested method could aid in mitigating this threat, and to eventually strengthen our model based on the data collected from the moves of the players.

Identity validation of Online Social Networks’ (OSNs’) peers is a critical concern to the insurance of safe and secure online socializing environments. Starting from the vision of empowering users to determine the validity of OSN identities, we suggest a framework to estimate the trustworthiness of online social profiles based only on the information they contain. Our framework is based on learning identity correlations between profile attributes in an OSN community and on collecting ratings from OSN community members to evaluate the trustworthiness of target profiles. Our system guarantees utility, user anonymity, impartiality in rating, and operability within the dynamics and continuous evolution of OSNs. In this article, we detail the system design, and we prove its correctness against these claimed quality properties. Moreover, we test its effectiveness, feasibility, and efficiency through experimentation on real-world datasets from Facebook and Google+, in addition to using the Adults UCI dataset.

Online Social Networks exploit a lightweight process to identify their users so as to facilitate their fast adoption. However, such convenience comes at the price of making legitimate users subject to different threats created by fake accounts. Therefore, there is a crucial need to empower users with tools helping them in assigning a level of trust to whomever they interact with. To cope with this issue, in this paper we introduce a novel model, DIVa, that leverages on mining techniques to find correlations among user profile attributes. These correlations are discovered not from user population as a whole, but from individual communities, where the correlations are more pronounced. DIVa exploits a decentralized learning approach and ensures privacy preservation as each node in the OSN independently processes its local data and is required to know only its direct neighbors. Extensive experiments using real-world OSN datasets show that DIVa is able to extract fine-grained community-aware correlations among profile attributes with average improvements up to 50% than the global approach.

Identity management in online social networks (OSNs) is a challenging, yet important requirement for effective privacy protection and trust management. Literature offers several proposals addressing issues related to identity breaches and/or identity related attacks on OSNs, but only a few aim at giving means to judge users' reliability in terms of trustworthiness of their claimed identities. In this paper, we propose an identity validation process that relies on OSN community feedback to assign to OSN users identity trustworthiness levels. For this purpose, we define a community based supervised learning process to detect the set of attributes in a user profile for which it is expected to see a correlation among their values (e.g., job and salary). Once these correlated attribute sets are identified, the profile of a target user is judged by a selected group of raters to estimate her identity trustworthiness level. We demonstrate the effectiveness of our proposal through experimentation under two different scenarios and using real data. The experiments' results under the two scenarios demonstrate the effectiveness and meaningfulness of our proposal.