Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

Empirical Study of Privacy Issues Among Social Networking Sites.

Social media networks are increasing their types of services and the numbers of users are rapidly growing. However, online consumers have expressed concerns about their personal privacy protection and recent news articles have shown many privacy breaches and unannounced changes to privacy policies. These events could adversely affect data protection and compromise user trust, thus it is vital that social sites contain explicit privacy policies stating a comprehensive list of protection methods. This study analyzes 60 worldwide social sites and finds that even if sites contain a privacy policy, the site pages may also possess technical elements that could be used to serendipitously collect personal information. The results show specific technical collection methods most common within several social network categories. Methods for improving online privacy practices are suggested

Evaluating the Contextual Integrity of Privacy Regulation: Parents' IoT Toy Privacy Norms Versus COPPA

Increased concern about data privacy has prompted new and updated data protection regulations worldwide. However, there has been no rigorous way to test whether the practices mandated by these regulations actually align with the privacy norms of affected populations. Here, we demonstrate that surveys based on the theory of contextual integrity provide a quantifiable and scalable method for measuring the conformity of specific regulatory provisions to privacy norms. We apply this method to the U.S. Children's Online Privacy Protection Act (COPPA), surveying 195 parents and providing the first data that COPPA's mandates generally align with parents' privacy expectations for Internet-connected "smart" children's toys. Nevertheless, variations in the acceptability of data collection across specific smart toys, information types, parent ages, and other conditions emphasize the importance of detailed contextual factors to privacy norms, which may not be adequately captured by COPPA.Comment: 18 pages, 1 table, 4 figures, 2 appendice

Digital Privacy: Personal Data Collection Methods and the Myth of Online Privacy

Mobile devices offer users a constant connection to information and entertainment. Our society has become hyperconnected. We have unprecedented access to information at any time of the day. Mobile devices have the potential to make people more efficient and productive or more distracted and negatively influenced. The use of applications or apps on mobile devices brings with them unparalleled access to intimate information about the users of mobile devices. Corporations have been quick to provide apps that make life easier for, or entertain, the end-users. But the entertainment and access come at a price. That price is incredibly detailed information about the users, and it is being used and sold on the internet. Companies are requiring users to allow mobile applications access to far more detailed information than is necessary, and the end-user is unaware of just what the price they are paying is. This paper will explore the permissions that mobile apps request, a company’s terms of service, and third-party relationships to determine if software manufacturers are honest with their stated permissions or if apps are overreaching in their efforts to collect information about their users. An examination of application permissions and analysis of the data transmissions to and from the device on behalf of the application will be performed. This work aims to provide users with more insight into how to protect their confidential data and to improve users’ perception of privacy

Antecedents And Consequences Of Consumers Online Privacy Concerns

This paper proposes a theoretical framework to investigate the factors that influence the privacy concerns of consumers who use the Internet, and the possible outcomes of such privacy concerns. Factors identified as antecedents to online privacy concerns are perceived vulnerability to personal data collection and misuse, perceived ability to control data collection and subsequent use, the level of Internet literacy, social awareness, and background cultural factors.  The possible consequences of online privacy concerns are the lack of willingness to provide personal information online, rejection of e-commerce, or even unwillingness to use the Internet.  Managerial implications of the framework are discussed

Crawling Facebook for Social Network Analysis Purposes

We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.\u

Reliable online social network data collection

Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users’ behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users’ behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.Postprin