145,879 research outputs found
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
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