In this section we propose P i x e l T r a c k, a classifier that identifiesĪll the tracking behaviors presented in Section 6 byĪnalyzing the HTTP requests and responses, and cookie storage. DS (Direct sharing), B64S (Base64 sharing), ES (Encrypted sharing), PCS (ID as part of the cookie), PPS (ID sent as part of the parameter) are different sharing techniques described in Figure 2. The arrows represent the follow of the cookie synchronization, ( →) one way matching or ( ↔) both ways matching. Table 3: Third to third party cookie syncing: Top 10 partners. Adsvr synced the same cookie with doubleclick, casalemedia and adnxs. What makes this practice even more harmful is that a third party can have more than one partner with whom it syncs cookies and therefore the user’s browsing history collected in that case is even more important.Īs example of this behavior we detected a cookie syncing between adsrvr and rubiconproject. Using cookie syncing a tracker not only log the user’s visit to the websites where it’s included but it can also log her visits to the websites where it’s partners are included. It’s true that a cross domain tracker recreates part of the user’s browsing history but this is only possible on the websites on which it was embedded. In fact, third party cookie syncing can be seen as set of trackers performing basic tracking and then exchanging the data they collected about the user. Impact: Third to third party cookie syncing is one of the most harmful tracking techniques that impacts the user’s privacy. The previous works demonstrate that there is no unified method to detect which third-party requests are tracking. That allows third-parties to merge users’ data across websites. Was applied at large scale only in the context of measuring cookie syncing, Heuristics to filter cookies that are likely to contain unique identifiers. Therefore, measuring only a number of third-party cookies definitely leads to a high number of false positives. However, it is well-known that cookies are used for various functionalities, and may contain non-unique values that are not useful for tracking. These studies were based on collecting all third-party cookies and analysing behavior associated to them. Other studies measured the mere presence of third-party cookies. One method to detect trackers is to analyse behaviour of HTTP requests and responses that set or send cookies and identify different classes of tracking, such as analytics or cross-domain tracking. The most known Web tracking technology is based on cookies, but not allĬookies are useful for cross-site tracking, and not all of them contain unique identifiers. Moreover, we find that if we combine both strategies, 238,439 requests (11%) originatedįrom 7,773 domains that still track users on 5,098 websites. We demonstrate that two blocking strategies – based on EasyList&EasyPrivacyĪnd on Disconnect lists – each miss 22% of the trackers that we detect. New collaborations between domains on the full dataset of Invisible pixels and use this classification to detect new categories of tracking and uncover We then propose a fine-grained classification of tracking based on the analysis of By crawling 829,349 webpages, we detect that third-party invisible pixels are widelyĭeployed: they are present on more than 83% of domains and constitute 37.22% of all third-party images. In this paper, we propose an alternative solution to detect trackers by analyzing behavior of invisible pixels thatĪre perfect suspects for tracking. However, there was always a suspicion that lists miss unknown trackers. To detect tracking, most of the research studies and user tools rely on Web tracking has been extensively studied over the last decade.
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