In this paper, we analyze and systematize the state-ofthe-art graph data privacy and utility techniques. Specifically, we propose and develop SecGraph (available at [1]), a uniform and open-source Secure Graph data sharing/publishing system. In SecGraph, we systematically study, implement, and evaluate 11 graph data anonymization algorithms, 19 data utility metrics, and 15 modern Structure-base...

The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Although such analysis can facilitate better understanding of sociological, behavioral, and other interesting phenomena, there is growing concern about personal privacy being breached, thereby requiring effective anonymization techniques. If we consider the social graph ...

Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, due to the sensitivity of information contained in such social graphs, they need to be properly anonymized before release. Most of the graph anonymization techniques that have been proposed to sanitize social graph data rely on the pertu...

In this paper, we study the impacts of non-Personal Identifiable Information (non-PII) on the privacy of graph data with attribute information (e.g., social networks data with users’ profiles (attributes)), namely Structure-Attribute Graph (SAG) data, both theoretically and empirically. Our main contributions are two-fold: (i) we conduct the first attribute-based anonymity analysis for SAG data...

In this paper, a method for anonymization of social networks by clustering of k-edge-connected subgraphs (SACK) is presented. Previous anonymization algorithms do not consider distribution of nodes in social network graph according to their attributes. SACk tries to focus on this aspect that probability of existence of an edge between two nodes is related to their attributes and this leads to a...

In recent years, there has been a significant increase in the use of graph-formatted data. Socials networks, among others, represent relationships among users and present interesting information for researches and other third-parties. The problem appears when someone wants to publicly release this information, especially in the case of social or healthcare networks. In these cases, it is essent...

Releasing anonymized social network data for analysis has been a popular idea among data providers. Despite evidence to the contrary the belief that anonymization will solve the privacy problem in practice refuses to die. This dissertation contributes to the field of social graph de-anonymization by demonstrating that even automated models can be quite successful in breaching the privacy of suc...

Social networks, patient networks, and email networks are all examples of graphs that can be studied to learn about information diffusion, community structure and different system processes; however, they are also all examples of graphs containing potentially sensitive information. While several anonymization techniques have been proposed for social network data publishing, they all apply the a...

Graph anonymization aims at reducing the ability of an attacker to identify nodes a graph by obfuscating its structural information. In k-anonymity, this means making each node indistinguishable from least other k-1 nodes. Simply stripping their identifying label is insufficient, as with enough knowledge can still recover identities. We propose algorithm enforce k-anonymity based on Szemerédi r...