6,226 research outputs found
Measuring third party tracker power across web and mobile
Third-party networks collect vast amounts of data about users via web sites
and mobile applications. Consolidations among tracker companies can
significantly increase their individual tracking capabilities, prompting
scrutiny by competition regulators. Traditional measures of market share, based
on revenue or sales, fail to represent the tracking capability of a tracker,
especially if it spans both web and mobile. This paper proposes a new approach
to measure the concentration of tracking capability, based on the reach of a
tracker on popular websites and apps. Our results reveal that tracker
prominence and parent-subsidiary relationships have significant impact on
accurately measuring concentration
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
On web user tracking of browsing patterns for personalised advertising
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Parallel, Emergent and Distributed Systems on 19/02/2017, available online: http://www.tandfonline.com/doi/abs/10.1080/17445760.2017.1282480On today’s Web, users trade access to their private data for content and services. App and service providers want to know everything they can about their users, in order to improve their product experience. Also, advertising sustains the business model of many websites and applications. Efficient and successful advertising relies on predicting users’ actions and tastes to suggest a range of products to buy. Both service providers and advertisers try to track users’ behaviour across their product network. For application providers this means tracking users’ actions within their platform. For third-party services following users, means being able to track them across different websites and applications. It is well known how, while surfing the Web, users leave traces regarding their identity in the form of activity patterns and unstructured data. These data constitute what is called the user’s online footprint. We analyse how advertising networks build and collect users footprints and how the suggested advertising reacts to changes in the user behaviour.Peer ReviewedPostprint (author's final draft
Integration of heterogeneous devices and communication models via the cloud in the constrained internet of things
As the Internet of Things continues to expand in the coming years, the need for services that span multiple IoT application domains will continue to increase in order to realize the efficiency gains promised by the IoT. Today, however, service developers looking to add value on top of existing IoT systems are faced with very heterogeneous devices and systems. These systems implement a wide variety of network connectivity options, protocols (proprietary or standards-based), and communication methods all of which are unknown to a service developer that is new to the IoT. Even within one IoT standard, a device typically has multiple options for communicating with others. In order to alleviate service developers from these concerns, this paper presents a cloud-based platform for integrating heterogeneous constrained IoT devices and communication models into services. Our evaluation shows that the impact of our approach on the operation of constrained devices is minimal while providing a tangible benefit in service integration of low-resource IoT devices. A proof of concept demonstrates the latter by means of a control and management dashboard for constrained devices that was implemented on top of the presented platform. The results of our work enable service developers to more easily implement and deploy services that span a wide variety of IoT application domains
Web Tracking: Mechanisms, Implications, and Defenses
This articles surveys the existing literature on the methods currently used
by web services to track the user online as well as their purposes,
implications, and possible user's defenses. A significant majority of reviewed
articles and web resources are from years 2012-2014. Privacy seems to be the
Achilles' heel of today's web. Web services make continuous efforts to obtain
as much information as they can about the things we search, the sites we visit,
the people with who we contact, and the products we buy. Tracking is usually
performed for commercial purposes. We present 5 main groups of methods used for
user tracking, which are based on sessions, client storage, client cache,
fingerprinting, or yet other approaches. A special focus is placed on
mechanisms that use web caches, operational caches, and fingerprinting, as they
are usually very rich in terms of using various creative methodologies. We also
show how the users can be identified on the web and associated with their real
names, e-mail addresses, phone numbers, or even street addresses. We show why
tracking is being used and its possible implications for the users (price
discrimination, assessing financial credibility, determining insurance
coverage, government surveillance, and identity theft). For each of the
tracking methods, we present possible defenses. Apart from describing the
methods and tools used for keeping the personal data away from being tracked,
we also present several tools that were used for research purposes - their main
goal is to discover how and by which entity the users are being tracked on
their desktop computers or smartphones, provide this information to the users,
and visualize it in an accessible and easy to follow way. Finally, we present
the currently proposed future approaches to track the user and show that they
can potentially pose significant threats to the users' privacy.Comment: 29 pages, 212 reference
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