Party Tracking Research Articles

After Third Party Tracking: Regulating the Harms of Behavioural Advertising Through Consumer Data Protection

After Third Party Tracking: Regulating the Harms of Behavioural Advertising Through Consumer Data Protection

Dissolving privacy, one merger at a time: Competition, data and third party tracking

Amid growing concern about the use and abuse of personal data over the last decade, there is an emerging suggestion that regulators may need to turn their attention towards the concentrations of power deriving from large-scale data accumulation. No longer the preserve of data protection or privacy law, personal data is receiving attention within competition and antitrust law. Recent mergers and acquisitions between large digital technology platforms have raised important questions about how these different areas intersect and how they can complement one another in order to protect consumer welfare while ensuring competitive markets.This paper draws attention to one particularly complicated kind of digital data-intensive industry: that of third party tracking, in which a firm does not (only or primarily) collect and process personal data of its own customers or users, but rather data from the users of other ‘first party’ services. Mergers and acquisitions between firms active in the third party tracking industry raise unique challenges for privacy and fundamental rights which are often missed in regulatory decisions and academic discussions of data and market concentration. In this paper, we combine empirical and normative insights to shed light on the role of competition regulators in addressing the specific challenges of mergers and acquisitions in the third party tracking industry. After critically assessing some of the US and EU case law in this area, we argue that a bolder approach is needed; one that engages in a pluralist analysis of economic and noneconomic concerns about concentrations of power and control over data.

Hybrid and lightweight detection of third party tracking: Design, implementation, and evaluation

A common practice for websites is to rely on services provided by third party sites to track users and provide personalized experiences. Unfortunately, this practice has strong implications for both users and performance. From one hand, the privacy of individuals is at a risk given the use of valuable information used for the reconstruction of personal profiles. From the other hand, many existing countermeasures to protect privacy, having been implemented into Web browsers, exhibit performance issues, mainly due to the use of huge (and difficult to maintain up to date) lists of resources that have to be filtered out, given their privacy intrusiveness.To overcome these limitations, we propose the use of a hybrid mechanism exploiting blacklisting and machine learning for the automatic identification of privacy intrusive services requested while browsing Web pages. The idea is to use the blacklisting technique (widely used by the majority of privacy tools), in combination with a machine learning model which distinguishes between malicious and functional resources, and hence updates the blacklist, accordingly. We found out that machine learning models are able to classify JavaScript programs and HTTP requests with accuracy up to 91% and 97%, respectively.We provided a prototype implementation of this hybrid mechanism, named GuardOne, and we performed an exhaustive evaluation study to assess its effectiveness and performance. Results showed that GuardOne is able to filter out malicious resources from users’ requests without performance degradation when compared with traditional systems that leverage on the use of static lists for filtering. Moreover, results about effectiveness show that our mechanism, even with some small improvements, is able to efficiently filter out malicious requests and reduce in a substantial way personal information leakage.

Acquisitions in the Third Party Tracking Industry: Competition and Data Protection Aspects

Amid growing concern about the use and abuse of personal data over the last decade, there is an emerging suggestion that regulators may need to turn their attention towards the concentrations of power deriving from large-scale data accumulation. No longer the preserve of data protection or privacy law, personal data is receiving attention within competition and antitrust law. Recent mergers and acquisitions between large digital technology platforms have raised important questions about how these different areas intersect and how they can complement one another in order to protect consumer welfare while ensuring competitive markets. This paper draws attention to one particularly complicated kind of digital data-intensive industry: that of third party tracking, in which a firm does not (only or primarily) collect and process personal data of its own customers or users, but rather data from the users of other ‘first party’ services. Mergers and acquisitions between firms active in the third party tracking industry raise unique challenges for privacy and fundamental rights which are often missed in regulatory decisions and academic discussions of data and market concentration. In this paper, we combine empirical and normative insights to shed light on the role of competition regulators in addressing the specific challenges of mergers and acquisitions in the third party tracking industry. After critically assessing some of the US and EU case law in this area, we argue that a bolder approach is needed; one that engages in a pluralist analysis of economic and noneconomic concerns about concentrations of power and control over data.

Web Tracking Detection System (TDS): An Effective Strategy to Reduce Systematic Monitoring and Profiling of User Habits Across Websites

This thesis is about tracking online user behavior. Tracking is a granular problem, that goes beyond the phenomenon of behavioral advertising. It takes place in the context of rapid technological, ocial and political developments. The main contributions of this thesis are:(1) A working definition for web tracking.(2) An opt-out mechanism for web tracking based on regular expressions repository in combinationwith persistent opt-out cookies.(3) We show that the current do-not-track-me register, an opt-out mechanism for online behavioraladvertising (OBA) based on opt-out cookies, of IAB/EASA falls short on it’s promise. The opt-out cookie expiration dates are inconsistent, often less then five years. This is in contrast with the NAIopt-out tool, where all opt-out cookies have a minimum expiration data of five years. Added tothat, opt-out behavior is collected by third parties.(4) We show that the interconnectedness between nodes expressed in the number of links per node is an indicator (clustering coefficient) for web tracking.(5) We show that the number off iltered nodes with TDS as a percentage of the total number of nodes is an indicator for web tracking.(6) Five rules for constraint based graph mining of HTTP header information show that with use of a confirmed web tracking repository, it is possible to identify new third party tracking activity with connectivity color maps and pattern matching.

The Research and Implementation of RRC Layer Connection Reconfiguration in TD-LTE System

In the TD-LTE system, RRC (Radio Resource Control) layer plays a very important role as the control center in the whole higher layer protocol. This paper introduces the function of RRC layer and mainly focuses on the RRC layer connection reconfiguration. This paper also illustrates design of function, signaling process and uses the third party terminal and tracking software to calculate the corresponding diagram in the domain of log, which checks the implementation of RRC layer connection and reconfiguration.