When people utilize social applications and services, their privacy suffers potential serious threat. In this paper, we present a novel, robust, and effective de-anonymization attack to mobility trace data and social data. First, we design a Unified Similarity (US) measurement which takes account of local and global structural characteristics of data, information obtained from auxiliary data, a...

Privacy protection appears as a fundamental concern when personal data is collected, stored, and published. Several anonymization methods have been proposed to address privacy issues in private datasets. Every anonymization method has at least one parameter to adjust the level of privacy protection considering some utility for the collected data. Choosing a desirable level of privacy protection...

We present GSUVis, a visualization tool designed to provide better understanding of location-based social network (LBSN) data. LBSN data is one of the most important sources of information for transportation, marketing, health, and public safety. LBSN data consumers are interested in accessing and analysing data that is as complete and as accurate as possible. However, LBSN data contains sensit...

In this paper, the problem of matching pairs of correlated random graphs with multi-valued edge attributes is considered. Graph matching problems of this nature arise in several settings of practical interest including social network deanonymization, study of biological data, web graphs, etc. An achievable region for successful matching is derived by analyzing a new matching algorithm that we r...

Over the years, the literature on individual data anonymization has burgeoned in many directions. Borrowing from several areas of other sciences, the current diversity of concepts, models and tools available contributes to understanding and fostering individual data dissemination in a privacy-preserving way, as well as unleashing new sources of information for the benefits of society at large. ...

Node similarity is a fundamental problem in graph analytics. However, node similarity between nodes in different graphs (inter-graph nodes) has not received a lot of attention yet. The inter-graph node similarity is important in learning a new graph based on the knowledge of an existing graph (transfer learning on graphs) and has applications in biological, communication, and social networks. I...

This paper identifies various concepts involved in social networks for anonymizing the original details of the user. We focus on the various methods that can be applied for applying the anonymization techniques. The methods used are re-identification, k-isomorphism, k-automorphism and k w -SDA. These methods are used to provide the security and privacy for each user and the community in the soc...

This document describes anonymization techniques for IP flow data and the export of anonymized data using the IP Flow Information Export (IPFIX) protocol. It categorizes common anonymization schemes and defines the parameters needed to describe them. It provides guidelines for the implementation of anonymized data export and storage over IPFIX, and describes an information model and Optionsbase...

Real-world traffic traces are crucial for Internet research, but only a very small percentage of traces collected are made public. One major reason why traffic trace owners hesitate to make the traces publicly available is the concern that confidential and private information may be inferred from the trace. In this paper we focus on the problem of anonymizing IP addresses in a trace. More speci...