Third Party Tracking in the Mobile Ecosystem

Third party tracking allows companies to identify users and track their behaviour across multiple digital services. This paper presents an empirical study of the prevalence of third-party trackers on 959,000 apps from the US and UK Google Play stores. We find that most apps contain third party tracking, and the distribution of trackers is long-tailed with several highly dominant trackers accounting for a large portion of the coverage. The extent of tracking also differs between categories of apps; in particular, news apps and apps targeted at children appear to be amongst the worst in terms of the number of third party trackers associated with them. Third party tracking is also revealed to be a highly trans-national phenomenon, with many trackers operating in jurisdictions outside the EU. Based on these findings, we draw out some significant legal compliance challenges facing the tracking industry.Comment: Corrected missing company info (Linkedin owned by Microsoft). Figures for Microsoft and Linkedin re-calculated and added to Table

Tackling poverty and disadvantage in schools: working with the community and other services

The link between disadvantage and educational underachievement is still strong. Most schools still fail to target support specifically at disadvantaged learners and only a few analyse data effectively enough to identify disadvantaged learners. Most schools do not use their assessment and tracking systems well enough to monitor the progress of disadvantaged learners. The few schools that support their disadvantaged learners well implement systematic, whole-school approaches for teaching and learning that benefit all learners and support individual disadvantaged learners by providing mentoring or help with basic skills and homework. Nearly all schools see themselves as community-focused and work with a range of agencies. However, school leaders do not usually co-ordinate multi-agency working systematically enough to ensure that disadvantaged learners are supported in the most effective and timely way. Only a few schools plan explicitly to raise disadvantaged learners’ aspirations. Although many schools offer a range of out-of-hours learning, only in a few are these extra activities carefully planned to increase disadvantaged learners’ confidence, motivation and self-esteem. Where schools have had the greatest impact on raising learners’ achievement, staff plan out-of-hours learning to match the needs of learners and to complement the curriculum. School leaders generally have not received enough training on working with the community or services, or on using data to evaluate initiatives to tackle disadvantage. Schools do not share best practice or collaborate effectively with each other in this area. Most local authorities do not do enough to offer schools practical guidance on how to work with local communities and services, or how best to analyse outcome data for disadvantaged learners. Local authorities that work systematically with schools to tackle poverty and disadvantage have the greatest impact on learner achievement

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

The Gaza Strip as Panopticon and Pansprectron: The Disciplining and Punishing of a Society\ud

This paper explores the different yet complementary aspects of the panopticon and the panspectron using the case study of the Israeli controlled Palestinian territory, the Gaza Strip. Beginning with a brief theoretical discussion of the concept of panopticon and panspectron expanding on the existing literature, the paper moves on to discuss the implementation of panoptical and panspectral technologies and practices in the Gaza Strip and situates these within a larger framework of control of the Palestinian population under Israeli occupation, and discusses seepage of these surveillance technologies into Israeli society proper and\ud beyond into the international arena.\u

F-formation Detection: Individuating Free-standing Conversational Groups in Images

Detection of groups of interacting people is a very interesting and useful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far neglected in the Computer Vision literature. On top of this taxonomy, we present a detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular we are able to codify in a computational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people. We call the proposed method Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all the state of the art methods in terms of different accuracy measures (some of them are brand new), demonstrating also a strong robustness to noise and versatility in recognizing groups of various cardinality.Comment: 32 pages, submitted to PLOS On

Joint optimisation of privacy and cost of in-app mobile user profiling and targeted ads

Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process and trade rich amount of consumer's personal data and carries out interests-based ads targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using internet services. In this paper, we address user's privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns and interest-based ads targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve equivalent problem using Lyapunov optimisation and to overcome difficulty of solving nonlinear programming by decomposing it into various cases and achieve trade-off between user privacy, cost and targeted ads. We carry out extensive experimentations and demonstrate proposed framework's applicability by implementing its critical components using POC `System App'. We compare proposed framework with other privacy protecting approaches and investigate that it achieves better privacy and functionality for various performance parameters

Online Personal Data Processing and EU Data Protection Reform. CEPS Task Force Report, April 2013

This report sheds light on the fundamental questions and underlying tensions between current policy objectives, compliance strategies and global trends in online personal data processing, assessing the existing and future framework in terms of effective regulation and public policy. Based on the discussions among the members of the CEPS Digital Forum and independent research carried out by the rapporteurs, policy conclusions are derived with the aim of making EU data protection policy more fit for purpose in today’s online technological context. This report constructively engages with the EU data protection framework, but does not provide a textual analysis of the EU data protection reform proposal as such

Recommender systems and their ethical challenges

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system