The Secure Link Prediction Problem

Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers which reveals sensitive information about the network. The graphs are therefore stored in encrypted form. We study the link prediction problem on encrypted graphs. To the best of our knowledge, this secure link prediction problem has not been studied before. We use the number of common neighbors for prediction. We present three algorithms for the secure link prediction problem. We design prototypes of the schemes and formally prove their security. We execute our algorithms in real-life datasets.Comment: This has been accepted for publication in Advances in Mathematics of Communications (AMC) journa

Adolescent beliefs about antisocial behavior : mediators and moderators of links with parental monitoring and attachment

The current study examined whether parental monitoring and attachment were related to adolescent beliefs about antisocial acts, with temperament, gender, and age considered as potential moderators. A total of 7135 adolescents, aged 14-18 years, completed selfreport measures of antisocial beliefs, parental monitoring, attachment security, and temperament. Results indicate that both attachment security and parental monitoring are associated with adolescent beliefs about antisocial behaviour. It also appears that the two aspects of parenting are complementary, in that a secure attachment relationship is associated with greater parental monitoring knowledge, which in turn is linked with a lower tolerance for antisocial behaviour. However, the relations between these aspects of parenting and beliefs about antisocial acts depended on the young people’s characteristics, with some results varying by age, gender and temperament. Implications for future research and parent-focused interventions to prevent antisocial beliefs and behaviour are discussed.peer-reviewe

Secure Satellite Communication Systems Design with Individual Secrecy Rate Constraints

In this paper, we study multibeam satellite secure communication through physical (PHY) layer security techniques, i.e., joint power control and beamforming. By first assuming that the Channel State Information (CSI) is available and the beamforming weights are fixed, a novel secure satellite system design is investigated to minimize the transmit power with individual secrecy rate constraints. An iterative algorithm is proposed to obtain an optimized power allocation strategy. Moreover, sub-optimal beamforming weights are obtained by completely eliminating the co-channel interference and nulling the eavesdroppers' signal simultaneously. In order to obtain jointly optimized power allocation and beamforming strategy in some practical cases, e.g., with certain estimation errors of the CSI, we further evaluate the impact of the eavesdropper's CSI on the secure multibeam satellite system design. The convergence of the iterative algorithm is proven under justifiable assumptions. The performance is evaluated by taking into account the impact of the number of antenna elements, number of beams, individual secrecy rate requirement, and CSI. The proposed novel secure multibeam satellite system design can achieve optimized power allocation to ensure the minimum individual secrecy rate requirement. The results show that the joint beamforming scheme is more favorable than fixed beamforming scheme, especially in the cases of a larger number of satellite antenna elements and higher secrecy rate requirement. Finally, we compare the results under the current satellite air-interface in DVB-S2 and the results under Gaussian inputs.Comment: 34 pages, 10 figures, 1 table, submitted to "Transactions on Information Forensics and Security

On Collaborative Predictive Blacklisting

Collaborative predictive blacklisting (CPB) allows to forecast future attack sources based on logs and alerts contributed by multiple organizations. Unfortunately, however, research on CPB has only focused on increasing the number of predicted attacks but has not considered the impact on false positives and false negatives. Moreover, sharing alerts is often hindered by confidentiality, trust, and liability issues, which motivates the need for privacy-preserving approaches to the problem. In this paper, we present a measurement study of state-of-the-art CPB techniques, aiming to shed light on the actual impact of collaboration. To this end, we reproduce and measure two systems: a non privacy-friendly one that uses a trusted coordinating party with access to all alerts (Soldo et al., 2010) and a peer-to-peer one using privacy-preserving data sharing (Freudiger et al., 2015). We show that, while collaboration boosts the number of predicted attacks, it also yields high false positives, ultimately leading to poor accuracy. This motivates us to present a hybrid approach, using a semi-trusted central entity, aiming to increase utility from collaboration while, at the same time, limiting information disclosure and false positives. This leads to a better trade-off of true and false positive rates, while at the same time addressing privacy concerns.Comment: A preliminary version of this paper appears in ACM SIGCOMM's Computer Communication Review (Volume 48 Issue 5, October 2018). This is the full versio