The intrusiveness and the increasing invasiveness of online advertising have,
in the last few years, raised serious concerns regarding user privacy and Web
usability. As a reaction to these concerns, we have witnessed the emergence of
a myriad of ad-blocking and anti-tracking tools, whose aim is to return control
to users over advertising. The problem with these technologies, however, is
that they are extremely limited and radical in their approach: users can only
choose either to block or allow all ads. With around 200 million people
regularly using these tools, the economic model of the Web ---in which users
get content free in return for allowing advertisers to show them ads--- is at
serious peril. In this paper, we propose a smart Web technology that aims at
bringing transparency to online advertising, so that users can make an informed
and equitable decision regarding ad blocking. The proposed technology is
implemented as a Web-browser extension and enables users to exert fine-grained
control over advertising, thus providing them with certain guarantees in terms
of privacy and browsing experience, while preserving the Internet economic
model. Experimental results in a real environment demonstrate the suitability
and feasibility of our approach, and provide preliminary findings on behavioral
targeting from real user browsing profiles.

本研究提出了一种智能 web 技术，旨在实现在线广告的透明化，帮助用户进行明智的决策和保护隐私，并证明了该方法的可行性和适用性。

MyAdChoices: 为在线广告带来透明度和控制

MyAdChoices: Bringing Transparency and Control to Online Advertising

The goal of privacy metrics is to measure the degree of privacy enjoyed by
users in a system and the amount of protection offered by privacy-enhancing
technologies. In this way, privacy metrics contribute to improving user privacy
in the digital world. The diversity and complexity of privacy metrics in the
literature makes an informed choice of metrics challenging. As a result,
instead of using existing metrics, new metrics are proposed frequently, and
privacy studies are often incomparable. In this survey we alleviate these
problems by structuring the landscape of privacy metrics. To this end, we
explain and discuss a selection of over eighty privacy metrics and introduce
categorizations based on the aspect of privacy they measure, their required
inputs, and the type of data that needs protection. In addition, we present a
method on how to choose privacy metrics based on nine questions that help
identify the right privacy metrics for a given scenario, and highlight topics
where additional work on privacy metrics is needed. Our survey spans multiple
privacy domains and can be understood as a general framework for privacy
measurement.

本文回顾了 80 多个隐私度量标准，介绍了衡量隐私度量标准的方面、必要输入、需要保护的数据类型，并提出了基于问题的选择隐私指标的方法，以及需要进一步研究隐私度量标准的主题。该研究跨越多个隐私领域，是一个可以理解为隐私测量的一般框架。