The Internet and social media have fueled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections, with particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question: Can individuals or groups actively manage their connections to evade social network analysis tools? By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence, and security agencies may better understand how terrorists escape detection. We first study how an individual can evade "network centrality" analysis without compromising his or her influence within the network. We prove that an optimal solution to this problem is hard to compute. Despite this hardness, we demonstrate that even a simple heuristic, whereby attention is restricted to the individual's immediate neighbourhood, can be surprisingly effective in practice. For instance, it could disguise Mohamed Atta's leading position within the WTC terrorist network, and that is by rewiring a strikingly-small number of connections. Next, we study how a community can increase the likelihood of being overlooked by community-detection algorithms. We propose a measure of concealment, expressing how well a community is hidden, and use it to demonstrate the effectiveness of a simple heuristic, whereby members of the community either "unfriend" certain other members, or "befriend" some non-members, in a coordinated effort to camouflage their community.

Our private connections can be exposed by link prediction algorithms. To date, this threat has only been addressed from the perspective of a central authority, completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by studying how an individual can rewire her own network neighborhood to hide her sensitive relationships. We prove that the optimization problem faced by such an individual is NP-complete, meaning that any attempt to identify an optimal way to hide one’s relationships is futile. Based on this, we shift our attention towards developing effective, albeit not optimal, heuristics that are readily-applicable by users of existing social media platforms to conceal any connections they deem sensitive. Our empirical evaluation reveals that it is more beneficial to focus on “unfriending” carefully-chosen individuals rather than befriending new ones. In fact, by avoiding communication with just 5 individuals, it is possible for one to hide some of her relationships in a massive, real-life telecommunication network, consisting of 829,725 phone calls between 248,763 individuals. Our analysis also shows that link prediction algorithms are more susceptible to manipulation in smaller and denser networks. Evaluating the error vs. attack tolerance of link prediction algorithms reveals that rewiring connections randomly may end up exposing one’s sensitive relationships, highlighting the importance of the strategic aspect. In an age where personal relationships continue to leave digital traces, our results empower the general public to proactively protect their private relationships.

Social media has made it possible to manipulate the masses via disinformation and fake news at an unprecedented scale. This is particularly alarming from a security perspective, as humans have proven to be one of the weakest links when protecting critical infrastructure in general, and the power grid in particular. Here, we consider an attack in which an adversary attempts to manipulate the behavior of energy consumers by sending fake discount notifications encouraging them to shift their consumption into the peak-demand period. Using Greater London as a case study, we show that such disinformation can indeed lead to unwitting consumers synchronizing their energy-usage patterns, and result in blackouts on a city-scale if the grid is heavily loaded. We then conduct surveys to assess the propensity of people to follow-through on such notifications and forward them to their friends. This allows us to model how the disinformation may propagate through social networks, potentially amplifying the attack impact. These findings demonstrate that in an era when disinformation can be weaponized, system vulnerabilities arise not only from the hardware and software of critical infrastructure, but also from the behavior of the consumers.