An automated web crawl methodology to analyze the online privacy landscape

Protecting end-users privacy and building trust are the two most important factors needed to support the growth of ecommerce. The increased dependence on the Internet for a wide variety of daily transactions causes a corresponding loss in privacy for many users, as virtually all websites collect data from users directly or indirectly while performing business with them. In this thesis I have used a web crawler named “iWatch” which serves as an instrument to collect basic statistics on the state of privacy, security, and data-collection practices on the web. I have looked at several interesting practices, and ways of examining the data. This thesis is also meant to serve as a point for reflection and discussion about which practices to observe, and how the raw data from such a system can and should be evolved and made available to a wider audience. The purpose of this thesis is to show web-crawling is a valid approach to mass data collection over the internet with the aim of predicting privacy practices and analyzing how they have evolved in the last three years in terms of geography, legislation, risks, biases and flows. Finally I demonstrate methods to show how to control bias while collecting data, and I propose a probabilistic mathematical model to limit the depth of search to achieve wider breadth for web crawling techniques in the future

Behavioral Targeting: A Case Study of Consumer Tracking on Levis.com

Behavioral targeting is an online marketing method that collects data on the browsing activities of consumers, in order to ‘target’ more relevant online advertising. It places digital tags in the browsers of web site visitors, using these tags to track and aggregate consumer behavior. The vast majority of data is collected anonymously, i.e., not linked to a person’s name. However, behavioral targeting does create digital dossiers on consumers with the aim of connecting browsing activity to a tagged individual. This tagging is largely invisible to consumers, who are not asked to explicitly give consent for this practice. By using data collected clandestinely, behavioral targeting undermines the autonomy of consumers in their online shopping and purchase decisions. In order to illustrate the nature of consumer tracking, a case study was conducted that examined behavioral targeting within Levis.com, the e-commerce site for the Levis clothing line. The results show the Levis web site loads a total of nine tracking tags that link to eight third party companies, none of which are acknowledged in the Levis privacy policy. Behavioral targeting, by camouflaging the tracking of consumers, can damage the perceived trustworthiness of an e-commerce site or the actor it represents. The risks behavioral targeting presents to trust within ecommerce are discussed, leading to recommendations to reestablish consumer control over behavioral targeting methods

Privacy Butler: A personal privacy rights manager for online presence.

The online presence projected by a person is comprised of all the information about them available on the Internet. In online communities and social networking services, it is often possible for third-parties to modify this content by, for example, commenting on existing content or uploading new content. This has the potential to negatively impact the privacy of a presence owner (the person referred to by the on-line content) by disclosing information about them without consent. In this paper we propose a Privacy Butler, an automated service that can monitor a persons online presence and attempt to make corrections based on policies specified by the owner of the online presence. © 2010 IEEE.Accepted versio

E-Mail Tracking: Status Quo and Novel Countermeasures

E-mail advertisement, as one instrument in the marketing mix, allows companies to collect fine-grained behavioural data about individual users’ e-mail reading habits realised through sophisticated tracking mechanisms. Such tracking can be harmful for user privacy and security. This problem is especially severe since e-mail tracking techniques gather data without user consent. Striving to increase privacy and security in e-mail communication, the paper makes three contributions. First, a large database of newsletter e-mails is developed. This data facilitates investigating the prevalence of e-mail tracking among 300 global enterprises from Germany, the United Kingdom and the United States. Second, countermeasures are developed for automatically identifying and blocking e-mail tracking mechanisms without impeding the user experience. The approach consists of identifying important tracking descriptors and creating a neural network-based detection model. Last, the effectiveness of the proposed approach is established by means of empirical experimentation. The results suggest a classification accuracy of 99.99%

Da una seed list al crawling e al grafo del web italiano

Il lavoro descritto in questa relazione è stato svolto all'interno di un progetto che ha lo scopo di valutare l'effettiva efficacia degli algoritmi di link analysis nell'individuare le pagine web di maggiore qualità. Il nostro lavoro è consistito nel contribuire al crawling esaustivo del web italiano per costruirne l'intero grafo; in particolare, ci siamo occupati della scelta di un insieme ottimale di documenti da cui esso ha avuto inizio (seed list) e della gestione del crawlin

Enhancing System Transparency, Trust, and Privacy with Internet Measurement

While on the Internet, users participate in many systems designed to protect their information’s security. Protection of the user’s information can depend on several technical properties, including transparency, trust, and privacy. Preserving these properties is challenging due to the scale and distributed nature of the Internet; no single actor has control over these features. Instead, the systems are designed to provide them, even in the face of attackers. However, it is possible to utilize Internet measurement to better defend transparency, trust, and privacy. Internet measurement allows observation of many behaviors of distributed, Internet-connected systems. These new observations can be used to better defend the system they measure. In this dissertation, I explore four contexts in which Internet measurement can be used to the aid of end-users in Internet-centric, adversarial settings. First, I improve transparency into Internet censorship practices by developing new Internet measurement techniques. Then, I use Internet measurement to enable the deployment of end-to-middle censorship circumvention techniques to a half-million users. Next, I evaluate transparency and improve trust in the Web public-key infrastructure by combining Internet measurement techniques and using them to augment core components of the Web public-key infrastructure. Finally, I evaluate browser extensions that provide privacy to users on the web, providing insight for designers and simple recommendations for end-users. By focusing on end-user concerns in widely deployed systems critical to end-user security and privacy, Internet measurement enables improvements to transparency, trust, and privacy.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163199/1/benvds_1.pd

The perception and fears of sharing personal digital data in digital public space

This thesis provides a critical and practice based investigation of personal fears of sharing personal digital data. In it, I explore the fears and growing tensions between the requirements to share personal information while maintaining the need to control and protect personal privacy. The emphasis of this study was to develop research through a series of multi-disciplinary, practice-based projects alongside external industry partners. I begin by exploring the rise in surveillance methods, from the Panopticon to the rise of social network sites and examine the consequences of sharing personal information online. Data sharing has been made easier through the proliferation of internet connected, mobile devices and wearable technologies that has led to a growing reciprocal trade in personal information in return for online services. In a world of ‘digital narcissism’ and perpetual life-logging brought about by the volume of shared data, modern surveillance is an increasingly manifestation of consumer activity. However, since the Snowden revelations in 2013 which revealed the National Security Agency (NSA) was spying on US citizens, the consequence of sharing personal information has led to a proliferation of leaks, thefts, and growing anxieties amongst the public, resulting in a greater awareness of privacy concerns and wariness about divulging personal information. My research focused upon those that obstruct, withhold information, and avoid contributing to sharing personal data. Therefore, my research was designed to identify the strategies available to designers working with shared data to combat fears of data surveillance and exploitation. The outcome of my research has shown, through a series of case studies, how individuals perceive the physical environment and the proximity to their data, and how data will be shared. My research was part of the innovative Creative Exchange programme, one of four Doctoral Training Centre knowledge exchange hubs funded by the Arts and Humanities Research Council. The aim was to develop research using multidisciplinary, practice based research projects alongside external industry partners, utilising a variety of research methods and co-design approaches to investigate concepts around the emergent subject of digital public space