scholarly journals Differential Privacy for Edge Weights in Social Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoye Li ◽  
Jing Yang ◽  
Zhenlong Sun ◽  
Jianpei Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Differential Privacy ◽  
Social Issues ◽  
Shortest Paths ◽  
Edge Weight ◽  
Sensitive Information ◽  
Original Order ◽  
Social Graphs ◽  
Inference Strategy

Social networks can be analyzed to discover important social issues; however, it will cause privacy disclosure in the process. The edge weights play an important role in social graphs, which are associated with sensitive information (e.g., the price of commercial trade). In the paper, we propose the MB-CI (Merging Barrels and Consistency Inference) strategy to protect weighted social graphs. By viewing the edge-weight sequence as an unattributed histogram, differential privacy for edge weights can be implemented based on the histogram. Considering that some edges have the same weight in a social network, we merge the barrels with the same count into one group to reduce the noise required. Moreover,k-indistinguishability between groups is proposed to fulfill differential privacy not to be violated, because simple merging operation may disclose some information by the magnitude of noise itself. For keeping most of the shortest paths unchanged, we do consistency inference according to original order of the sequence as an important postprocessing step. Experimental results show that the proposed approach effectively improved the accuracy and utility of the released data.

scholarly journals Structural Attack to Anonymous Graph of Social Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Tieying Zhu ◽  
Shanshan Wang ◽  
Xiangtao Li ◽  
Zhiguo Zhou ◽  
Riming Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Graph Matching ◽  
Rapid Development ◽  
Sensitive Information ◽  
Matching Method ◽  
Graph Data ◽  
Social Network Data ◽  
Regional Feature ◽  
Social Graphs

With the rapid development of social networks and its applications, the demand of publishing and sharing social network data for the purpose of commercial or research is increasing. However, the disclosure risks of sensitive information of social network users are also arising. The paper proposes an effective structural attack to deanonymize social graph data. The attack uses the cumulative degree ofn-hop neighbors of a node as the regional feature and combines it with the simulated annealing-based graph matching method to explore the nodes reidentification in anonymous social graphs. The simulation results on two social network datasets show that the attack is feasible in the nodes reidentification in anonymous graphs including the simply anonymous graph, randomized graph andk-isomorphism graph.

Intranet Exploitation of Social Network Knowledge Intelligence

2017 ◽  
pp. 273-298
Author(s):  
Mark Alan Underwood
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Life Cycle ◽  
Online Social Networks ◽  
Intelligent Agents ◽  
Process Mining ◽  
Just In Time ◽  
Domain Specific ◽  
Social Graphs

Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.

scholarly journals Differentially Private Recommendation System Based on Community Detection in Social Network Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Gesu Li ◽  
Zhipeng Cai ◽  
Guisheng Yin ◽  
Zaobo He ◽  
Madhuri Siddula
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Recommendation System ◽  
Detection Method ◽  
Personal Information ◽  
The Internet ◽  
Sensitive Information ◽  
Network Applications ◽  
Target Attributes

The recommender system is mainly used in the e-commerce platform. With the development of the Internet, social networks and e-commerce networks have broken each other’s boundaries. Users also post information about their favorite movies or books on social networks. With the enhancement of people’s privacy awareness, the personal information of many users released publicly is limited. In the absence of items rating and knowing some user information, we propose a novel recommendation method. This method provides a list of recommendations for target attributes based on community detection and known user attributes and links. Considering the recommendation list and published user information that may be exploited by the attacker to infer other sensitive information of users and threaten users’ privacy, we propose the CDAI (Infer Attributes based on Community Detection) method, which finds a balance between utility and privacy and provides users with safer recommendations.

Finding the Trustworthiness Nodes from Signed Social Networks

2013 ◽  
Vol 22 (4) ◽  
pp. 471-485
Author(s):  
Hui Li ◽  
Shu Zhang ◽  
Xia Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Network ◽  
Network Services ◽  
Edge Weight ◽  
The Public ◽  
Signed Social Networks ◽  
Public Dataset ◽  
Negative Relationships ◽  
The Prestige

AbstractOnline social network services have brought a kind of new lifestyle to the world that is parallel to people’s daily offline activities. Social network analysis provides a useful perspective on a range of social computing applications. Social interaction on the Web includes both positive and negative relationships, which is certainly important to social networks. The authors of this article found that the accuracy of the signs of links in the underlying social networks can be predicted. The trust that other users impart on a node is an important attribute of networks. In this article, the authors present a model to compute the prestige of nodes in a trust-based network. The model is based on the idea that trustworthy nodes weigh more. To fulfill this task, the authors first attempt to infer the attitude of one user toward another by predicting signed edges in networks. Then, the authors propose an algorithm to compute the prestige and trustworthiness where the edge weight denotes the trust score. To prove the algorithm’s effectiveness, the authors conducted experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective.

Anonymization Techniques for Privacy Preservation in Social Networks: A Review

2021 ◽  
pp. 14-21
Author(s):  
Kalpana Chavhan ◽  
Dr. Praveen S. Challagidad
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Privacy Preservation ◽  
Information Science ◽  
Original Data ◽  
Published Data ◽  
Sensitive Information ◽  
Social Network Data ◽  
The Social

Any data that user creates or owns is known as the user's data (For example: Name, USN, Phone number, address, email Id). As the number of users in social networks are increasing day by day the data generated by the user's is also increasing. Network providers will publish the data to others for analysis with hope that mining will provide additional functionality to their users or produce useful results that they can share with others. The analysis of social networks is used in modern sociology, geography, economics and information science as well as in various fields. Publicizing the original data of social networks for analysis raises issues of confidentiality, the adversary can search for documented threats such as identity theft, digital harassment and personalized spam. The published data may contain some sensitive information of individuals which must not be disclosed for this reason social network data must be anonymized before it is published. To do the data in nominate the anonymization technique should be applied, to preserve the privacy of data in the social network in a manner that preserves the privacy of the user whose records are being published while maintaining the published dataset rich enough to allow for the exploration of data. In order to address the issue of privacy protection, we first describe the concept of k-anonymity and illustrate different approaches for its enforcement. We then discuss how the privacy requirements characterized by k-anonymity can be violated in data mining and introduce possible approaches to ensure the satisfaction of k-anonymity in data mining also several attacks on dataset are discussed.

Social Network Anonymization

2018 ◽  
pp. 23-35
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real Data ◽  
Electronic Records ◽  
Sensitive Information ◽  
Social Scientists ◽  
High Importance ◽  
Privacy Threats ◽  
Technological Advances ◽  
Undesirable Consequence

Due to technological advances, it has become easy to collect electronic records from a social network for an adversary. However, the organisations which collect data from social networks have two options before them: either they can publish the data and bear the undesirable consequence of privacy threats or not make it public by avoiding further analysis of these data by social scientists to uncover useful facts, which can be of high importance for the society. Since both these options are undesirable, one can try to find an intermediate way between the two, that is, the data before publishing can be anonymised such that even if an adversary gets some information from the published network, he/she cannot decipher and obtain sensitive information about any individual. By anonymization, the authors mean the perturbation of the real data in order to make it undecipherable. This chapter explores social network anonymization.

SOCIAL NETWORKS AS A MECHANISM FOR INCLUDING RESIDENTS IN SOCIAL PARTICIPATION PRACTICES OF TERRITORY DEVELOPMENT

2021 ◽  
Vol 7 (4) ◽  
pp. 69-84
Author(s):  
Anna N. TARASOVA ◽  
Ekaterina A. KOSTROVA
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Participation ◽  
Cultural Development ◽  
Living Conditions ◽  
Social Connections ◽  
Territorial Development ◽  
Important Conclusion ◽  
Social Communities ◽  
Social Graphs

The article is devoted to the study of the role of social networks in development of a territory through the inclusion of individuals in social communities that influence the socio-economic, socio-cultural development of cities and regions. A social network in the present article refers to the structure of social relations and relationships between people, based on respect, common interests and mutual assistance. The study reveals some interconnections between indicators of a given social network (as structures of interaction between people) and living conditions (as characteristics of the level of territory development), and also shows through which social connections a person is most often included in social participation practices that contribute to development of a territory. In the study authors used the method of correlation analysis of the open database of the “Legatum Institute” analytical center. Another method consists of analysis of social graphs based on data from the social networking service “Vkontakte”. Weak-moderate but statistically significant associations are found between indicators of territorial development and indicators that determine quality of a social network (level of respect, harmony of interests and assistance to others). The study revealed a mechanism of multidirectional relationships between social networks and living conditions. Through this mechanism, territorial development can be ensured even in situations of crisis and economic instability. Analysis of social graphs showed that social networks differ while being linked to people who are socially active and socially passive in the number of social connections, in the number of social communities in which they are included, in the type of these communities. An important conclusion was made that university more often promotes inclusion in social participation practices precisely through development of new connections in a social network. While school systems contribute little to this. There is also a relatively low level of inclusion of youth in activities of territorial communities (POA, courtyard, housing, city communities, etc.), which requires a further detailed study.

scholarly journals Multi-mode Network Analysis under Differential Privacy

2021 ◽  
Vol 2082 (1) ◽  
pp. 012010
Author(s):  
Yuning Song ◽  
Liping Ding ◽  
Mengying Dong ◽  
Xuehua Liu ◽  
Xiao Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Differential Privacy ◽  
Personal Information ◽  
Rapid Development ◽  
Experimental Simulation ◽  
The Public ◽  
Centrality Analysis ◽  
Multi Mode

Abstract With the advent of the big data era and the advancement of social network analysis, the public is increasingly concerned about the privacy protection in today’s complex social networks. For the past few years, the rapid development of differential privacy (DP) technology, as a method with a reliable theoretical basis, can effectively solve the key problem of how to “disassociate” personal information in social networks. This paper focuses on the multi-mode heterogeneous network model which has attracted a lot of attention in the field of network research. It introduces differential privacy and its application in big social networks briefly first, and then proposes a centrality-analysis method based on DP in a typical social network, i.e. the multi-mode network. The calculation principle and applicable scenarios are discussed. Then, its utility is analyzed and evaluated through experimental simulation. Possible improvement of DP algorithm in multi-mode networks above is prospected in the end.

scholarly journals Perspective of understanding for Children’s Privacy and Security

2015 ◽  
Vol 3 (8) ◽  
pp. 91-96
Author(s):  
Lina M Cook ◽  
Jianjun Yang ◽  
Ju Shen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Issues ◽  
Personal Information ◽  
Network Activity ◽  
Sex Crimes ◽  
Privacy And Security ◽  
Technological Revolution ◽  
And Control ◽  
Network Administrators

The thirteen years of age requirement to open any social network account does not guarantee that children under this age would not have access to social networks such as Facebook, Snapchat, Instagram, or YouTube just to mention some. The reason for this is that children want to be a part of this technological revolution, and create these accounts by lying about their age, or are aid by their parents without realizing the consequences this action might bring. While these social networks disclose that they delete information belonging to children under 13 from their databases as soon as they are aware of it, it is questionable if they are doing enough to protect children’s privacy , and if they should be liable for any incident related to this issue since such is widely known, but apparently neglected. Unfortunately, by social network administrators not reinforcing the age requirement policy, and launching features that makes its user’s personal information available to third parties, are indirectly exposing children with fraudulent accounts at risk of being victims of more serious ethical and social issues such as cyber bulling, and sex crimes. While it is determined that social network has little control over this issue, all which is left to do, is to provide parents with education and control of their children’s social network activity interaction to protect their privacy and keep them safe.

scholarly journals Securing Heterogeneous Privacy Protection in Social Network Records based Encryption Scheme

2019 ◽  
Vol 7 (3) ◽  
pp. 10-13
Author(s):  
Balamurugan. R ◽  
Dhivakar. M ◽  
Muruganantham. G ◽  
Ramprakash. S
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Network Inference ◽  
Third Party ◽  
Sensitive Information ◽  
Privacy And Security ◽  
User Data

This survey places of interest the major issues concerning privacy and security in online social networks. Firstly, we discuss investigate that aims to protect user data from the an assortment of attack vantage points together with other users, advertisers, third party request developers, and the online social arrangement provider itself. Next we cover social network supposition of user attributes, locate hubs, and link prediction. Because online social networks are so saturated with sensitive information, network inference plays a major privacy role. Social Networking sites go upwards since of all these reasons. In recent years indicates that for many people they are now the mainstream communication knowledge. Social networking sites come under few of the most frequently browsed categories websites in the world. Nevertheless Social Networking sites are also vulnerable to various problems threats and attacks such as revelation of information, identity thefts etc. Privacy practice in social networking sites often appear convoluted as in sequence sharing stands in discord with the need to reduce disclosure-related abuses. Facebook is one such most popular and widely used Social Networking sites which have its own healthy set of Privacy policy.

Sign in / Sign up

Export Citation Format

Share Document