BPRS: Belief Propagation Based Iterative Recommender System

In this paper we introduce the first application of the Belief Propagation (BP) algorithm in the design of recommender systems. We formulate the recommendation problem as an inference problem and aim to compute the marginal probability distributions of the variables which represent the ratings to be predicted. However, computing these marginal probability functions is computationally prohibitive for large-scale systems. Therefore, we utilize the BP algorithm to efficiently compute these functions. Recommendations for each active user are then iteratively computed by probabilistic message passing. As opposed to the previous recommender algorithms, BPRS does not require solving the recommendation problem for all the users if it wishes to update the recommendations for only a single active. Further, BPRS computes the recommendations for each user with linear complexity and without requiring a training period. Via computer simulations (using the 100K MovieLens dataset), we verify that BPRS iteratively reduces the error in the predicted ratings of the users until it converges. Finally, we confirm that BPRS is comparable to the state of art methods such as Correlation-based neighborhood model (CorNgbr) and Singular Value Decomposition (SVD) in terms of rating and precision accuracy. Therefore, we believe that the BP-based recommendation algorithm is a new promising approach which offers a significant advantage on scalability while providing competitive accuracy for the recommender systems

SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection

Sybil attacks are a fundamental threat to the security of distributed systems. Recently, there has been a growing interest in leveraging social networks to mitigate Sybil attacks. However, the existing approaches suffer from one or more drawbacks, including bootstrapping from either only known benign or known Sybil nodes, failing to tolerate noise in their prior knowledge about known benign or Sybil nodes, and being not scalable. In this work, we aim to overcome these drawbacks. Towards this goal, we introduce SybilBelief, a semi-supervised learning framework, to detect Sybil nodes. SybilBelief takes a social network of the nodes in the system, a small set of known benign nodes, and, optionally, a small set of known Sybils as input. Then SybilBelief propagates the label information from the known benign and/or Sybil nodes to the remaining nodes in the system. We evaluate SybilBelief using both synthetic and real world social network topologies. We show that SybilBelief is able to accurately identify Sybil nodes with low false positive rates and low false negative rates. SybilBelief is resilient to noise in our prior knowledge about known benign and Sybil nodes. Moreover, SybilBelief performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.Comment: 12 page

A Formal Model for Trust in Dynamic Networks

We propose a formal model of trust informed by the Global Computing scenario and focusing on the aspects of trust formation, evolution, and propagation. The model is based on a novel notion of trust structures which, building on concepts from trust management and domain theory, feature at the same time a trust and an information partial order

The SECURE collaboration model

The SECURE project has shown how trust can be made computationally tractable while retaining a reasonable connection with human and social notions of trust. SECURE has produced a well-founded theory of trust that has been tested and refined through use in real software such as collaborative spam filtering and electronic purse. The software comprises the SECURE kernel with extensions for policy specification by application developers. It has yet to be applied to large-scale, multi-domain distributed systems taking different application contexts into account. The project has not considered privacy in evidence distribution, a crucial issue for many application domains, including public services such as healthcare and police. The SECURE collaboration model has similarities with the trust domain concept, embodying the interaction set of a principal, but SECURE is primarily concerned with pseudonymous entities rather than domain-structured systems