NoMoATS: Towards Automatic Detection of Mobile Tracking

Abstract

Today’s mobile apps employ third-party advertising and tracking (A&T) libraries, which may pose a threat to privacy. State-of-the-art detects and blocks outgoing A&T HTTP/S requests by using manually curated filter lists (e.g. EasyList), and recently, using machine learning approaches. The major bottleneck of both filter lists and classifiers is that they rely on experts and the community to inspect traffic and manually create filter list rules that can then be used to block traffic or label ground truth datasets. We propose NoMoATS – a system that removes this bottleneck by reducing the daunting task of manually creating filter rules, to the much easier and scalable task of labeling A&T libraries. Our system leverages stack trace analysis to automatically label which network requests are generated by A&T libraries. Using NoMoATS, we collect and label a new mobile traffic dataset. We use this dataset to train decision tree classifiers, which can be applied in real-time on the mobile device and achieve an average F-score of 93%. We show that both our automatic labeling and our classifiers discover thousands of requests destined to hundreds of different hosts, previously undetected by popular filter lists. To the best of our knowledge, our system is the first to (1) automatically label which mobile network requests are engaged in A&T, while requiring to only manually label libraries to their purpose and (2) apply on-device machine learning classifiers that operate at the granularity of URLs, can inspect connections across all apps, and detect not only ads, but also tracking.

Joint Optimisation of Privacy and Cost of in-App Mobile User Profiling and Targeted Ads

“…The newly selected candidate apps, which we call the obfuscation apps (note that in this work, we interchangeably use both 'newly suggested apps' and 'obfuscation apps'), are selected (and run for specific amount of time, as described in Eq. 23, (24), and (25)) from apps categories Φ j , j = 1, ..., φ other than the private category Φ p ; Φ p is considered private by users that they want to protect. The newly suggested set S o comprises those apps with highest similarity to the existing set of apps i.e.…”

“…s.t. constants: (13), (14), (15), (17), (24). An important challenge to solve this optimisation problem is to know user's temporal behavior as a combined activity of 'Web & App' i.e.…”

“…s.t. constants: (16), (17), (24). Now the main challenge of solving objective (31) is to minimise the variance of U t (K a ) in order to protect user's app's usage behavior i.e.…”

Privacy-preserving targeted mobile advertising:A Blockchain-based framework for mobile ads

“…direct and indirect (inferred) leakage, have been investigated extensively in literature, e.g. [1], [43], [44], [45], including third party ad tracking and visiting [46], [47], [48], [49]. These studies have shown the prevalence of tracking on both web and mobile environments and demonstrate the possibility of inferring user's PII, such as age, gender, relationship status, email address, etc.…”

Privacy in targeted advertising: A survey

“…direct and inferred leakage), have been examined extensively in literature, e.g. [10], [97], including third party ad tracking and visiting [98], [99].…”