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.

Networks

2020 ◽  
pp. 534-562
Author(s):  
Matthew O. Jackson ◽  
Brian W. Rogers ◽  
Yves Zenou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Statistical Analysis ◽  
Labor Markets ◽  
Network Formation ◽  
Labor Market Outcomes ◽  
Negative Consequences ◽  
Social Scientists ◽  
Market Outcomes

What is the role of social networks in driving persistent differences between races and genders in education and labor market outcomes? What is the role of homophily in such differences? Why is such homophily seen even if it ends up with negative consequences in terms of labor markets? This chapter discusses social network analysis from the perspective of economics. The chapter is organized around the theme of externalities: the effects that one’s behavior has on others’ welfare. Externalities underlie the interdependencies that make networks interesting to social scientists. This chapter discusses network formation, as well as interactions between people’s behaviors within a given network, and the implications in a variety of settings. Finally, the chapter highlights some empirical challenges inherent in the statistical analysis of network-based data.

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.

Marketing based on user behavior on Facebook social network through recommender system design

2015 ◽  
Vol 3 ◽  
pp. 27 ◽  
Author(s):  
Bahareh Shadi Shams Zamenjani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommender Systems ◽  
Recommender System ◽  
User Behavior ◽  
Real Data ◽  
User Interests ◽  
Second Stage ◽  
Commercial Use ◽  
High Level

t— the influence of social networks among people and at the same time inevitable spread of commercial use of them. Accordingly, in order to sell products, recommender systems designed based on user behavior on social networks, providing a variety of commercial offers tailored to the user. The accuracy of recommender systems that make recommendations to users, and how many of the proposals are accepted by the users is important. In this paper, a recommender system is designed based on user behavior in social network Facebook in two acts and suggests that users purchase their favorite products. The first step is to examine user behavior based on user interests will be given an offer to buy products. In the second stage recommender system uses data mining techniques and suggestions to the user that is associated with their previous purchases. This is real data and the real results of it and it is valid, as well as the results show a high level of accuracy recommender system is designed to offer suggestions to users.

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.

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.

scholarly journals Online social networks security and privacy: comprehensive review and analysis

2021 ◽  
Author(s):  
Ankit Kumar Jain ◽  
Somya Ranjan Sahoo ◽  
Jyoti Kaubiyal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Web Applications ◽  
Security And Privacy ◽  
Privacy Threats ◽  
Shared Information ◽  
Personal Content ◽  
Privacy Issues ◽  
Open Issues

AbstractWith fast-growing technology, online social networks (OSNs) have exploded in popularity over the past few years. The pivotal reason behind this phenomenon happens to be the ability of OSNs to provide a platform for users to connect with their family, friends, and colleagues. The information shared in social network and media spreads very fast, almost instantaneously which makes it attractive for attackers to gain information. Secrecy and surety of OSNs need to be inquired from various positions. There are numerous security and privacy issues related to the user’s shared information especially when a user uploads personal content such as photos, videos, and audios. The attacker can maliciously use shared information for illegitimate purposes. The risks are even higher if children are targeted. To address these issues, this paper presents a thorough review of different security and privacy threats and existing solutions that can provide security to social network users. We have also discussed OSN attacks on various OSN web applications by citing some statistics reports. In addition to this, we have discussed numerous defensive approaches to OSN security. Finally, this survey discusses open issues, challenges, and relevant security guidelines to achieve trustworthiness in online social networks.

Social Networks Applied to E-Gov

2010 ◽  
pp. 637-651
Author(s):  
Leandro Pupo Natale ◽  
Ismar Frango Silveira ◽  
Wagner Luiz Zucchi ◽  
Pollyana Notargiacomo Mustaro
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Unified Modeling Language ◽  
Modeling Language ◽  
Graphical Display ◽  
Unified Modeling ◽  
Technological Advances ◽  
The Social ◽  
Software Engineering Process ◽  
The Government

The technological advances establish new communication forms between people and have also reached the government sphere and its activities, improving access to information and allowing greater interaction between citizens through C2C (Citizen to Citizen) Services. Based on these aspects, this chapter presents a proposal for software architecture, using a social network to map the relationships and interactions between citizens, accounting and storing this knowledge in a government ontological metadata network. Using UML notation (Unified Modeling Language) for Software Engineering process and Java platform for development, a software prototype was modeled and developed in order to manage and handle e-Gov-driven social networks, using ontological metadata to computationally represent the social ties. This prototype is also capable of providing graphical display of social networks, enabling the identification of different social links between citizens, creating a tool intended for government agencies, since it allows a quantitative analysis of information in the social network.

Social Networks Applied to E-Gov

2010 ◽  
pp. 802-818
Author(s):  
Leandro Pupo Natale ◽  
Ismar Frango Silveira ◽  
Wagner Luiz Zucchi ◽  
Pollyana Notargiacomo Mustaro
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Unified Modeling Language ◽  
Modeling Language ◽  
Graphical Display ◽  
Unified Modeling ◽  
Technological Advances ◽  
The Social ◽  
Software Engineering Process ◽  
The Government

The technological advances establish new communication forms between people and have also reached the government sphere and its activities, improving access to information and allowing greater interaction between citizens through C2C (Citizen to Citizen) Services. Based on these aspects, this chapter presents a proposal for software architecture, using a social network to map the relationships and interactions between citizens, accounting and storing this knowledge in a government ontological metadata network. Using UML notation (Unified Modeling Language) for Software Engineering process and Java platform for development, a software prototype was modeled and developed in order to manage and handle e-Gov-driven social networks, using ontological metadata to computationally represent the social ties. This prototype is also capable of providing graphical display of social networks, enabling the identification of different social links between citizens, creating a tool intended for government agencies, since it allows a quantitative analysis of information in the social network.

A METHOD OF A SOCIAL GRAPH BUILDING IN A GIVEN FIELD OF ACTIVITY USING DATA FROM A SOCIAL NETWORK

2021 ◽  
pp. 76-81
Author(s):  
Е.Е. Лунева ◽  
А.А. Ефремов ◽  
Ю.А. Лобода
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real Data ◽  
Social Graph ◽  
Using Data

В статье описывается способ построения социального графа по данным из социальных сетей в определенной сфере деятельности, опирающийся на активность пользователей в социальных сетях. В статье приводятся данные эксперимента позволяющего оценить точность воспроизведения реальных связей между пользователями в социальном графе. The article provides a method of a social graph construction based on data from social networks in a certain field of activity, as well as on the activity of users in social networks. Furthermore, the paper presents the findings of real data experiment that allows evaluating the accuracy of reproduction of real connections between users in a social graph.

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.

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