Protecting privacy through metadata analysis

Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we demonstrate the power of metadata analysis. We illustrate ways in which we can use metadata analysis to protect privacy, by addressing two problems in the areas of network and web privacy.In the first problem, we study recently standardized protocols such as encrypted DNS and QUIC. We show how metadata analysis can be used by adversaries to perform website-fingerprinting attacks against these protocols and to infer websites visited by a user, thereby compromising their privacy. We use the insights from our analysis to identify the requirements for developing effective countermeasures and to improve the resistance of these protocols to website fingerprinting. We find that hiding metadata is challenging.In the second problem, we address the issue of online advertising and tracking services (ATS) that are constantly evolving to evade privacy protections established by browser vendors. We demonstrate how the very fact of metadata being hard to hide can be useful to defenders. Defenders can use metadata analysis to build ATS-detection systems that are more robust against adversarial evasion by capturing behavioral metadata of ATS. We use our findings to detect and counter the emergence of tracking via first-party cookies.

Protecting Privacy in Multi-agent Optimization

Eric Zbinden

Many real-life optimization problems involve multiple entities, or agents (individuals, companies...), with their own private constraints and preferences, communicating with each other in order to find a solution that maximizes the overall public satisfaction. In Artificial Intelligence, the field of Distributed Constraint Optimization (DCOP) has been addressing such multi-agent optimization problems, through distributed message-passing algorithms such as the DPOP algorithm. However, the research in DCOP has been neglecting the privacy of the information exchanged by the agents during the computing of the solution, which is critical to many real-life problems. While agents are willing to cooperate with each other to produce an optimal solution, they are most often reluctant to reveal their private constraints and preferences to other, which hinders this cooperation. The goal of this project was to implement, test, and evaluate a secured version of DPOP, P-DPOP. P-DPOP provides strong agent privacy and topology privacy by randomization, constraints privacy and limited decision privacy by obfuscation. The algorithm was implemented in Java, as part of the open-source FRODO platform for DCOP.

2010

Security, Privacy and Economics of Online Advertising

Nevena Vratonjic

Online advertising is at the core of today’s Web: it is the main business model, generating large annual revenues expressed in tens of billions of dollars that sponsor most of the online content and services. Online advertising consists of delivering marketing messages, embedded into Web content, to a targeted audience. In this model, entities attract Web traffic by offering the content and services for free and charge advertisers for including advertisements in this traffic (i.e., advertisers pay for users’ attention and interests). Online advertising is a very successful form of advertising as it allows for advertisements (ads) to be targeted to individual users’ interests; especially when advertisements are served on users’ mobile devices, as ads can be targeted to users’ locations and the corresponding context. However, online advertising also introduces a number of problems. Given the high ad revenue at stake, fraudsters have economic incentives to exploit the ad system and generate profit from it. Unfortunately, to achieve this goal, they often compromise users’ online security (e.g., via malware, phishing, etc.). For the purpose of maximizing the revenue by matching ads to users’ interests, a number of techniques are deployed, aimed at tracking and profiling users’ digital footprints, i.e., their behavior in the digital world. These techniques introduce new threats to users’ privacy. Consequently, some users adopt ad-avoidance tools that prevent the download of advertisements and partially thwart user profiling. Such user behavior, as well as exploits of ad systems, have economic implications as they undermine the online advertising business model. Meddling with advertising revenue disrupts the current economic model of the Web, the consequences of which are unclear. Given that today’s Web model relies on online advertising revenue in order for users to have access and consume content and services for “free”, coupled with the fact that there are many threats that could jeopardize this model, in this thesis we address the security, privacy and economic issues stemming from this fundamental element of the Web. In the first part of the thesis, we investigate the vulnerabilities of online advertising systems. We identify how an adversary can exploit the ad system to generate profit for itself, notably by performing inflight modification of ad traffic. We provide a proof-of-concept implementation of the identified threat on Wi-Fi routers. We propose a collaborative approach for securing online advertising and Web browsing against such threats. By investigating how a certificate-based authentication is deployed in practice, we assess the potential of relying on certificate-based authentication as a building block of a solution to protect the ad revenue. We propose a multidisciplinary approach for improving the current state of certificate-based authentication on the Web. In the second part of the thesis, we study the economics of ad systems’ exploits and certain potential countermeasures. We evaluate the potential of different solutions aimed at protecting ad revenue being implemented by the stakeholders (e.g., Internet Service Providers or ad networks) and the conditions under which this is likely to happen. We also study the economic ramifications of ad-avoidance technologies on the monetization of online content. We use game-theory to model the strategic behavior of involved entities and their interactions. In the third part of the thesis, we focus on privacy implications of online advertising. We identify a novel threat to users’ location privacy that enables service providers to geolocate users with high accuracy, which is needed to serve location-targeted ads for local businesses. We draw attention to the large scale of the threat and the potential impact on users’ location privacy.

EPFL2013

MyAdChoices: Bringing Transparency and Control to Online Advertising

Jagdish Prasad Achara

The intrusiveness and the increasing invasiveness of online advertising have, in the last few years, raised serious concerns regarding user privacy andWeb usability. As a reaction to these concerns, we have witnessed the emergence of a myriad of ad-blocking and antitracking 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 article, 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.

Assoc Computing Machinery2017

Security, Privacy and Economics of Online Advertising

Nevena Vratonjic

EPFL2013