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A popular means of social communication for online users has become a trend with rapid growth of social networks in the last few years. Facebook, Myspace, Twitter, LinkedIn, etc. have created huge amounts of data about interactions of social networks. Meanwhile, the trend is also true for offline scenarios with rapid growth of mobile devices such as smart phones, tablets, and laptops used for social interactions. These mobile devices enlarge the traditional social network services platform and lead to a greater amount of mobile social network data. These data contain more private information of individuals such as location, habit, and health condition. However, there are many analytical, sociological, and economic questions that can be answered using these data, so the mobility data managers are expected to share the data with researchers, governments, and/or companies. Therefore, mobile social network data is badly in need of anonymization before it is shared or analyzed widely. k-anonymization is a well-known clustering-based anonymization approach. However, the implementation of this basic approach has been a challenge since many of the mobile social network data involve categorical data values. In this paper, we propose an approach for categorical data clustering using rough entropy method with DBSCAN clustering algorithm to improve the performance of k-anonymization approach. It has the ability to deal with uncertainty in the clustering process and can effectively find arbitrarily shaped clusters. We will report the proposed approach and discuss the credibility by theoretical studies and examples. And experimental results on two benchmark data sets obtained from UCI Machine Learning Repository show that our approach is second to none among the Fuzzy Centroids, MMeR, SDR and ITDR, etc. with respect to the local and global purity of clusters. Since the clustering algorithm is a key point of k-anonymization for clustering mobile social network data, our experimental results show that our proposed algorithm can be more effective to balance the utility of the mobile social network data and the performance of anonymization.

Edge computing has developed rapidly in recent years due to its advantages of low bandwidth overhead and low delay, but it also brings challenges in data security and privacy. Website fingerprinting (WF) is a passive traffic analysis attack that threatens website privacy which poses a great threat to user’s privacy and web security. It collects network packets generated while a user accesses website, and then uses a series of techniques to discover patterns of network packets to infer the type of website user accesses. Many anonymous networks such as Tor can meet the need of hide identity from users in network activities, but they are also threatened by WF attacks. In this paper, we propose a website fingerprinting obfuscation method against intelligent fingerprinting attacks, called Random Bidirectional Padding (RBP). It is a novel website fingerprinting defense technology based on time sampling and random bidirectional packets padding, which can covert the real packets distribution to destroy the Inter-Arrival Time (IAT) features in the traffic sequence and increase the difference between the datasets with random bidirectional virtual packets padding. We evaluate the defense against state-of-the-art website fingerprinting attacks in real scenarios, and show its effectiveness.

Part 2: The 2014 Asian Conference on Availability, Reliability and Security, AsiaARES 2014International audienceWith the development of internet technology, more and more risks are appearing on the internet and the internet security has become an important issue. Intrusion detection technology is an important part of internet security. In intrusion detection, it is important to have a fast and effective method to find out known and unknown attacks. In this paper, we present a graph-based intrusion detection algorithm by outlier detection method which is based on local deviation factor (LDFGB). This algorithm has better detection rates than a previous clustering algorithm. Moreover, it is able to detect any shape of cluster and still keep high detection rate for detecting unknown or known attacks. LDFGB algorithm uses graph-based cluster algorithm (GB) to get an initial partition of dataset which depends on a parameter of cluster precision, then we use the outlier detection algorithm to further processing the results of graph-based cluster algorithm. This measure is effective to improve the detection rates and false positive rates

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