Eindhoven University of Technology MASTER Content based access control in social networks sites van den Munckhof, C.J.J. Award date: 2011 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain MASTER THESIS Content Based Access Control in Social Network Sites EINDHOVEN UNIVERSITY OF TECHNOLOGY DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE Author: Supervisors: Coen VAN DEN MUNCKHOF Dr. J.I. (Jerry) DEN HARTOG Prof. Dr. R.E. (Ronald) LEENES September 2011 Abstract A string of incidents on the news involving users of social network sites (SNSs) that unknowingly expose their private information to the public form the basis of this Master Thesis. It seems that many people are unaware of the audiences that have access to the content that they share on these SNSs or that they are unmotivated to pay attention to privacy policies for their content. This is the main problem addressed in this thesis. First an extensive research is done that includes an analysis of existing SNSs (containing a definition, feature description, and a detailed look at their privacy settings and data management). The research phase continues with a detailed look at the available access control mechanisms (methods that determine who are granted access to certain digital content and who are not). The research phase leads to the proposition of a new method driven by content based access control. The method automatically proposes an appro- priate privacy policy for a user’s content. It does so by analyzing both the content of the message and the author’s profile and distilling keywords (or tags) that are matching with those from an existing dataset. This set of so called attributes is then matched with an existing dataset of privacy policies to determine the most appropriate policy. The thesis ends with an experi- ment in which an essential element of the proposed method is tested using data from Hyves and Wikipedia. Contents List of Figures ix List of Tables xi 1 Introduction 1 1.1 SNS Related Incidents . .2 1.2 Project Outline . .3 1.2.1 The Problem . .3 1.2.2 The Scope . .3 1.2.3 The Research Question . .4 1.3 Structure . .4 2 Social Network Sites 7 2.1 Popularity . .7 2.2 Philosophy and Different Types of SNSs . .7 2.3 Information on SNSs . .8 2.4 Privacy Settings . 10 2.5 Conclusion . 13 3 Access Control 15 3.1 Access Control Models . 17 3.1.1 Discretionary Access Control . 17 3.1.2 Mandatory Access Control . 18 3.1.3 Role-Based Access Control . 19 v 3.2 Access Control Implementations . 20 3.2.1 Attribute Based Access Control . 20 3.2.2 Content Based Access Control . 21 3.2.3 XACML . 21 3.3 Conclusion . 22 4 Structured Data 23 4.1 Taxonomies & Ontologies . 23 4.2 Existing Datastructures . 24 4.3 Conclusion . 26 5 Content Based Access Control 29 5.1 Current Model . 30 5.2 Content Based Model . 30 5.3 Policy Proposing Process . 31 5.3.1 Determine Attributes . 32 5.3.2 Select policy from attributes . 32 6 Concept Testing 35 6.1 Implementation for the Experiment . 35 6.1.1 Attributes Determination . 35 6.1.2 Select Policy from Attributes . 38 6.1.3 Internal Datasets . 38 6.1.4 External Datasets . 39 6.1.5 Solr and Content Querying . 39 6.2 Preparation Datasets . 40 6.2.1 Tag Data from Wikipedia . 40 6.2.2 Message and Profile Data from Hyves . 41 6.2.3 Policy data . 42 6.3 Analysis . 42 6.3.1 Evaluation of the Wiki Data . 42 6.3.2 Evaluation of the Hyves Data . 44 6.3.3 Attribute Matching Evaluation . 47 vi 7 Conclusion 49 7.1 Conclusion . 49 7.2 Recommendations . 49 A Interesting Data in Wikipedia 51 B Solr Matching 53 B.1 Regular Expressions . 53 Bibliography 55 vii viii List of Figures 1.1 Project structure . .5 2.1 Tree of the data categories . .9 2.2 Hyves friends model . 11 2.3 Facebooks custom privacy popup . 12 3.1 Mandatory Access Control . 19 3.2 XACML access request flow . 22 5.1 Friends model . 30 5.2 Content based model . 31 5.3 Working of Content based access control . 32 6.1 Attribute determination from message content . 36 6.2 Policy determination from content attributes . 38 6.3 Example of a blogpost . 41 6.4 Example of ‘who what where’ message . 41 6.5 Part of the policy dataset . 42 6.6 Hyves users year of birth . 45 6.7 Friends count Hyves users . 45 A.1 Wikipedia’s infobox and visualization . 51 ix x List of Tables 6.1 Solr matches . 39 6.2 Category data in the Wikipedia tables . 43 6.3 Items fetched from Hyves . 44 6.4 Ratio of public and hidden profiles . 44 6.5 Gender distribution of fetched profiles . 44 6.6 Percentage of profiles with a hometown or city filled . 45 6.7 Five most popular cities . 46 6.8 Ten most popular regions of interest in sport . 46 6.9 Percentage of www messages with location field filled . 47 6.10 Tags in blogs . 47 6.11 Tags in status messages . 47 6.12 Date & Time attributes in status messages . 48 6.13 Date & Time attributes in blog posts . 48 A.1 Infobox locations . 51 xi xii 1. Introduction A news item from AFP [9] of March 15, 2011 reported the following: An Australian schoolgirl had to cancel her 16th birthday party after her Facebook invitation went viral and close to 200; 000 people said they would turn up at her house. The Sydney girl had wanted her schoolmates to attend, and the post – which included her address – said they could bring friends if they let her know, Sydney’s Daily Telegraph newspaper reported. “(It’s an) open house party as long as it doesn’t get out of hand,” she wrote, adding that she had not had time to invite everyone individually. But within 24 hours more than 20; 000 people had replied to the public event to say they were attending and by Tuesday almost 200; 000 potential partygoers had reportedly accepted the invita- tion. The girl’s father said his daughter had invited “a few friends” over Facebook but had initially been unaware of the settings required to stop strangers from viewing the information. This is just one of the many [8, 17, 39] examples reporting about the con- sequences of poorly configured privacy settings within Facebook1 or other social network sites (SNSs). Ten years ago, you wouldn’t have found this kind of news items. Back then, you would invite your friends by phone, by sending them an invitation by (electronic) mail or by direct conversations. Either way, you did not have to worry about who might see the invitation. In this case, the ‘Sydney girl’ probably did not realize that publicly posting this invitation could reach so many people in such a short time. SNSs have become a popular and widely used medium on the internet for sharing interests, thoughts, photos, activities etc. But besides all the nice features they provide, it also introduces new privacy issues. 1http://www:facebook:com 1 Chapter 1. Introduction SNSs heavily rely on their users to create the proper access control policies. But many users, just as the Sidney girl, are unaware of defining privacy settings. Other users might be aware but find them too much of a hassle to configure, or the given configuration possibilities too limited. And of course there is a group of users that simply does not care about their privacy settings. 1.1 SNS Related Incidents In 2008, Weblog Ask The Judge [44] reported that several law schools in the U.S. are using SNSs as part of their admissions process. Fifteen percent of the surveyed admissions officers admitted to visit profiles of applicants on SNSs and many found negative content that reflected poorly on the student. The SNSs allowed the admissions officers to freely investigate the applicants’ private lives. In early 2009, a teenager was fired from her job after negatively comment- ing on it on Facebook. The girl explained: “They (her superiors) were just being nosey, going through everything”, and further, “it makes them look stupid that they are going to be so petty”. Her boss however stated that she had posted her comments and invited staff members to read them [39]. In November 2009 a Canadian woman, who took long-term sick leave from her job, lost her health benefits after her insurance company visited her Face- book profile and had seen pictures of her during a male strip-tease show at a Chippendales bar, celebrating her birthday and pictures of her smiling in bikini at the beach.