Social Network De-Anonymization and Privacy Inference with Knowledge Graph Model

Qian, Jianwei; Li, Xiangyang; Zhang, Chunhong; Chen, Linlin; Jung, Taeho; Han, Junze

doi:10.1109/tdsc.2017.2697854

Cited by 72 publications

(30 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The framework of knowledge graph machine learning algorithms [60]. A KG is typically built on top of the existing databases to link all data together at webscale combining both structured and unstructured data, including objects, abstract concepts, numbers, and documents.…”

Section: What Is the Knowledge Graphmentioning

confidence: 99%

A survey on the development status and application prospects of knowledge graph in smart grids

Jian

Wang

et al. 2020

IET Generation Trans & Dist

View full text Add to dashboard Cite

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

show abstract

Section: What Is the Knowledge Graphmentioning

confidence: 99%

A survey on the development status and application prospects of knowledge graph in smart grids

Jian

Wang

et al. 2020

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…Beside structure of graphs, many works utilize the other information as side information to enhance the performance of graph deanonymization, such as community [8,27], attributes [17], semantics [29,30]. However, these methods also face the problem that a large amount of prior information might be difficult to obtain.…”

Section: Related Workmentioning

confidence: 99%

Seed-free Graph De-anonymiztiation with Adversarial Learning

Luo

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

The huge amount of graph data are published and shared for research and business purposes, which brings great benefit for our society. However, user privacy is badly undermined even though user identity can be anonymized. Graph de-anonymization to identify nodes from an anonymized graph is widely adopted to evaluate users' privacy risks. Most existing de-anonymization methods which are heavily reliant on side information (e.g., seeds, user profiles, community labels) are unrealistic due to the difficulty of collecting this side information. A few graph de-anonymization methods only using structural information, called seed-free methods, have been proposed recently, which mainly take advantage of the local and manual features of nodes while overlooking the global structural information of the graph for de-anonymization. In this paper, a seed-free graph de-anonymization method is proposed, where a deep neural network is adopted to learn features and an adversarial framework is employed for node matching. To be specific, the latent representation of each node is obtained by graph autoencoder. Furthermore, an adversarial learning model is proposed to transform the embedding of the anonymized graph to the latent space of auxiliary graph embedding such that a linear mapping can be derived from a global perspective. Finally, the most similar node pairs in the latent space as the anchor nodes are utilized to launch propagation to de-anonymize all the remaining nodes. The extensive experiments on some real datasets demonstrate that our method is comparative with the seed-based approaches and outperforms the start-of-the-art seed-free method significantly.

show abstract

“…The social graphs of social networks are usually explored for marketing or data mining and that can reveal individuals' sensitive information. Thus, there have been many researches to anonymise social graphs before they are published [35][36][37]. However, these researches have just focused on unweighted graphs though in practise, social graphs are weighted as the strengths of relationships of people in social networks are different.…”

Section: Privacy In Publishing Social Graphsmentioning

confidence: 99%

Privacy issues in social networks and analysis: a comprehensive survey

Pham

Sood

et al. 2018

IET Networks

View full text Add to dashboard Cite

Social networks have become part of today's life; however, they also create numerous privacy problems for their users as they reveal sensitive information. Thus, the privacy preservation has become a big problem in social networks and there are several researches related to this topic. However, the current research achievements for this issue are only ad hoc solutions and the broad picture of the problem is not clear. This paper surveys a wide range of related researches and realises that the topic includes many sub-areas; for example, the privacy in publishing social network data for the use of third-party consumers or the privacy of users in the leakage of individuals' information to unexpected people in their social circle. The study also analyses the advantages as well as limitations of proposed solutions. On the basis of that the research outlines key outstanding issues and recommends directions for the future research. Moreover, the research also investigates common privacy metrics and popular privacy-preserving techniques to provide readers some basic tools to resolve open problems in the topic. The ultimate purpose of this research is to pave a solid background for people, who are interested in the topic, to do research further.

show abstract

Social Network De-Anonymization and Privacy Inference with Knowledge Graph Model

Cited by 72 publications

References 48 publications

A survey on the development status and application prospects of knowledge graph in smart grids

A survey on the development status and application prospects of knowledge graph in smart grids

Seed-free Graph De-anonymiztiation with Adversarial Learning

Privacy issues in social networks and analysis: a comprehensive survey

Contact Info

Product

Resources

About