A Measurement Study on the Advertisements Displayed to Web Users Coming from the Regular Web and from Tor

Online advertising is an effective way for businesses to find new customers and expand their reach to a great variety of audiences. Due to the large number of participants interacting in the process, advertising networks act as brokers between website owners and businesses facilitating the display of advertisements. Unfortunately, this system is abused by cybercriminals to perform illegal activities such as malvertising. In this paper, we perform a measurement of malvertising from the user point of view. Our goal is to collect advertisements from a regular Internet connection and using The Onion Router in an attempt to understand whether using different technologies to access the Web could influence the probability of infection. We compare the data from our experiments to find differences in the malvertising activity observed. We show that the level of maliciousness is similar between the two types of accesses. Nevertheless, there are significant differences related to the malicious landing pages delivered in each type of access. Our results provide the research community with insights into how ad traffic is treated depending on the way users access Web content

Measuring Abuse in Web Push Advertising

The rapid growth of online advertising has fueled the growth of ad-blocking software, such as new ad-blocking and privacy-oriented browsers or browser extensions. In response, both ad publishers and ad networks are constantly trying to pursue new strategies to keep up their revenues. To this end, ad networks have started to leverage the Web Push technology enabled by modern web browsers. As web push notifications (WPNs) are relatively new, their role in ad delivery has not been yet studied in depth. Furthermore, it is unclear to what extent WPN ads are being abused for malvertising (i.e., to deliver malicious ads). In this paper, we aim to fill this gap. Specifically, we propose a system called PushAdMiner that is dedicated to (1) automatically registering for and collecting a large number of web-based push notifications from publisher websites, (2) finding WPN-based ads among these notifications, and (3) discovering malicious WPN-based ad campaigns. Using PushAdMiner, we collected and analyzed 21,541 WPN messages by visiting thousands of different websites. Among these, our system identified 572 WPN ad campaigns, for a total of 5,143 WPN-based ads that were pushed by a variety of ad networks. Furthermore, we found that 51% of all WPN ads we collected are malicious, and that traditional ad-blockers and malicious URL filters are remarkably ineffective against WPN-based malicious ads, leaving a significant abuse vector unchecked

Online Privacy in Mobile and Web Platforms: Risk Quantification and Obfuscation Techniques

The wide-spread use of the web and mobile platforms and their high engagement in human lives pose serious threats to the privacy and confidentiality of users. It has been demonstrated in a number of research works that devices, such as desktops, mobile, and web browsers contain subtle information and measurable variation, which allow them to be fingerprinted. Moreover, behavioural tracking is another form of privacy threat that is induced by the collection and monitoring of users gestures such as touch, motion, GPS, search queries, writing pattern, and more. The success of these methods is a clear indication that obfuscation techniques to protect the privacy of individuals, in reality, are not successful if the collected data contains potentially unique combinations of attributes relating to specific individuals. With this in view, this thesis focuses on understanding the privacy risks across the web and mobile platforms by identifying and quantifying the privacy leakages and then designing privacy preserving frameworks against identified threats. We first investigate the potential of using touch-based gestures to track mobile device users. For this purpose, we propose and develop an analytical framework that quantifies the amount of information carried by the user touch gestures. We then quantify users privacy risk in the web data using probabilistic method that incorporates all key privacy aspects, which are uniqueness, uniformity, and linkability of the web data. We also perform a large-scale study of dependency chains in the web and find that a large proportion of websites under-study load resources from suspicious third-parties that are known to mishandle user data and risk privacy leaks. The second half of the thesis addresses the abovementioned identified privacy risks by designing and developing privacy preserving frameworks for the web and mobile platforms. We propose an on-device privacy preserving framework that minimizes privacy leakages by bringing down the risk of trackability and distinguishability of mobile users while preserving the functionality of the existing apps/services. We finally propose a privacy-aware obfuscation framework for the web data having high predicted risk. Using differentially-private noise addition, our proposed framework is resilient against adversary who has knowledge about the obfuscation mechanism, HMM probabilities and the training dataset