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.
Besides, Zheng @cite_11 proposed link prediction in decentralized social network preserving the privacy. Their construction split the link score into private and public parts and applied sparse logistic regression to find links based on the content of the users. However, the graph data was not considered to be encrypted in the privacy preserving link prediction schemes.
@cite_11
2293003800 2783547004 2002163386 2507252477
We consider the privacy-preserving link prediction problem in decentralized online social network (OSNs). We formulate the problem as a sparse logistic regression problem and solve it with a novel decentralized two-tier method using alternating direction method of multipliers (ADMM). This method enables end users to collaborate with their online service providers without jeopardizing their data privacy. The method also grants end users fine-grained privacy control to their personal data by supporting arbitrary public private data split. Using real-world data, we show that our method enjoys various advantages including high prediction accuracy, balanced workload, and limited communication overhead. Additionally, we demonstrate that our method copes well with link reconstruction attack. Various paradigms, based on differential privacy, have been proposed to release a privacy-preserving dataset with statistical approximation. Nonetheless, most existing schemes are limited when facing highly correlated attributes, and cannot prevent privacy threats from untrusted servers. In this paper, we propose a novel Copula- based scheme to efficiently synthesize and release multi-dimensional crowdsourced data with local differential privacy. In our scheme, each participant's (or user's) data is locally transformed into bit strings based on a randomized response technique, which guarantees a participant's privacy on the participant (user) side. Then, Copula theory is leveraged to synthesize multi-dimensional crowdsourced data based on univariate marginal distribution and attribute dependence. Univariate marginal distribution is estimated by the Lasso-based regression algorithm from the aggregated privacy- preserving bit strings. Dependencies among attributes are modeled as multivariate Gaussian Copula, of which parameter is estimated by Pearson correlation coefficients. We conduct experiments to validate the effectiveness of our scheme. Our experimental results demonstrate that our scheme is effective for the release of multi-dimensional data with local differential privacy guaranteed to distributed participants. The concern of privacy has become an important issue for online social networks. In services such as Foursquare.com, whether a person likes an article is considered private and therefore not disclosed; only the aggregative statistics of articles (i.e., how many people like this article) is revealed. This paper tries to answer a question: can we predict the opinion holder in a heterogeneous social network without any labeled data? This question can be generalized to a link prediction with aggregative statistics problem. This paper devises a novel unsupervised framework to solve this problem, including two main components: (1) a three-layer factor graph model and three types of potential functions; (2) a ranked-margin learning and inference algorithm. Finally, we evaluate our method on four diverse prediction scenarios using four datasets: preference (Foursquare), repost (Twitter), response (Plurk), and citation (DBLP). We further exploit nine unsupervised models to solve this problem as baselines. Our approach not only wins out in all scenarios, but on the average achieves 9.90 AUC and 12.59 NDCG improvement over the best competitors. The resources are available at http: www.csie.ntu.edu.tw d97944007 aggregative We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure, but may be of relatively small order with a high multiplicity of edges. Building on recent work of Beveridge and Shan [4], we consider graph extraction, visualization, and network statistics for three novels: Twilight by Stephanie Meyer, Steven King’s The Stand, and J.K. Rowling’s Harry Potter and the Goblet of Fire. Coupling with 800 character networks from films found in the http: moviegalaxies.com database, we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees (also known as the Chung-Lu model), the configuration model, and the preferential attachment model. Using machine learning techniques based on motif (or small subgraph) counts, we determine that the Chung-Lu model best fits character networks and we conjecture why this may be the case.