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1. NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking (The Andreas Pfitzmann Best Student Paper Award)

   Shuba, A. ; Markopoulou, A. ; Shafiq, Z. ( October 2018 , Proceedings of the Privacy Enhancing Technologies Symposium (PETS))

   Although advertising is a popular strategy for mobile app monetization, it is often desirable to block ads in order to improve usability, performance, privacy, and security. In this paper, we propose NoMoAds to block ads served by any app on a mobile device. NoMoAds leverages the network interface as a universal vantage point: it can intercept, inspect, and block outgoing packets from all apps on a mobile device. NoMoAds extracts features from packet headers and/or payload to train machine learning classifiers for detecting ad requests. To evaluate NoMoAds, we collect and label a new dataset using both EasyList and manually created rules. We show that NoMoAds is effective: it achieves an F-score of up to 97.8% and performs well when deployed in the wild. Furthermore, NoMoAds is able to detect mobile ads that are missed by EasyList (more than one-third of ads in our dataset). We also show that NoMoAds is efficient: it performs ad classification on a per-packet basis in real-time. To the best of our knowledge, NoMoAds is the first mobile ad-blocker to effectively and efficiently block ads served across all apps using a machine learning approach. 

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2. A privacy‐preserving mobile location‐based advertising system for small businesses

   https://doi.org/10.1002/eng2.12416  

   Ahmed, Ahmed Abdelmoamen ( May 2021 , Engineering Reports)

   Small‐to‐medium businesses are always seeking affordable ways to advertise their products and services securely. With the emergence of mobile technology, it is possible than ever to implement innovative Location‐Based Advertising (LBS) systems using smartphones that preserve the privacy of mobile users. In this paper, we present a prototype implementation of such systems by developing a distributed privacy‐preserving system, which has parts executing on smartphones as a mobile app, as well as a web‐based application hosted on the cloud. The mobile app leverages Google Maps libraries to enhance the user experience in using the app. Mobile users can use the app to commute to their daily destinations while viewing relevant ads such as job openings in their neighborhood, discounts on favorite meals, etc. We developed a client‐server privacy architecture that anonymizes the mobile user trajectories using a bounded perturbation strategy. A multi‐modal sensing approach is proposed for modeling the context switching of the developed LBS system, which we represent as a Finite State Machine model. The multi‐modal sensing approach can reduce the power consumed by mobile devices by automatically detecting sensing mode changes to avoid unnecessary sensing. The developed LBS system is organized into two parts: the business side and the user side. First, the business side allows business owners to create new ads by providing the ad details, Geo‐location, photos, and any other instructions. Second, the user side allows mobile users to navigate through the map to see ads while walking, driving, bicycling, or quietly sitting in their offices. Experimental results are presented to demonstrate the scalability and performance of the mobile side. Our experimental evaluation demonstrates that the mobile app incurs low processing overhead and consequently has a small energy footprint.

    

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3. adPerf: Characterizing the Performance of Third-party Ads

   https://doi.org/10.1145/3447381  

   Pourghassemi, Behnam ; Bonecutter, Jordan ; Li, Zhou ; Chandramowlishwaran, Aparna ( February 2021 , Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   null (Ed.)

   Monetizing websites and web apps through online advertising is widespread in the web ecosystem, creating a billion-dollar market. This has led to the emergence of a vast network of tertiary ad providers and ad syndication to facilitate this growing market. Nowadays, the online advertising ecosystem forces publishers to integrate ads from these third-party domains. On the one hand, this raises several privacy and security concerns that are actively being studied in recent years. On the other hand, the ability of today's browsers to load dynamic web pages with complex animations and Javascript has also transformed online advertising. This can have a significant impact on webpage performance. The latter is a critical metric for optimization since it ultimately impacts user satisfaction. Unfortunately, there are limited literature studies on understanding the performance impacts of online advertising which we argue is as important as privacy and security. In this paper, we apply an in-depth and first-of-a-kind performance evaluation of web ads. Unlike prior efforts that rely primarily on adblockers, we perform a fine-grained analysis on the web browser's page loading process to demystify the performance cost of web ads. We aim to characterize the cost by every component of an ad, so the publisher, ad syndicate, and advertiser can improve the ad's performance with detailed guidance. For this purpose, we develop a tool, adPerf, for the Chrome browser that classifies page loading workloads into ad-related and main-content at the granularity of browser activities. Our evaluations show that online advertising entails more than 15% of browser page loading workload and approximately 88% of that is spent on JavaScript. On smartphones, this additional cost of ads is 7% lower since mobile pages include fewer and well-optimized ads. We also track the sources and delivery chain of web ads and analyze performance considering the origin of the ad contents. We observe that 2 of the well-known third-party ad domains contribute to 35% of the ads performance cost and surprisingly, top news websites implicitly include unknown third-party ads which in some cases build up to more than 37% of the ads performance cost. 

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4. Differentially-Private Next-Location Prediction with Neural Networks

   Ahuja, Ritesh ; Ghinita, Gabriel ; Shahabi, Cyrus ( March 2020 , Advances in database technology)

   The emergence of mobile apps (e.g., location-based services, geo-social networks, ride-sharing) led to the collection of vast amounts of trajectory data that greatly benefit the understanding of individual mobility. One problem of particular interest is next-location prediction, which facilitates location-based advertising, point-of-interest recommendation, traffic optimization,etc. However, using individual trajectories to build prediction models introduces serious privacy concerns, since exact whereabouts of users can disclose sensitive information such as their health status or lifestyle choices. Several research efforts focused on privacy-preserving next-location prediction, but they have serious limitations: some use outdated privacy models (e.g., k-anonymity), while others employ learning models with limited expressivity (e.g., matrix factorization). More recent approaches(e.g., DP-SGD) integrate the powerful differential privacy model with neural networks, but they provide only generic and difficult-to-tune methods that do not perform well on location data, which is inherently skewed and sparse.We propose a technique that builds upon DP-SGD, but adapts it for the requirements of next-location prediction. We focus on user-level privacy, a strong privacy guarantee that protects users regardless of how much data they contribute. Central to our approach is the use of the skip-gram model, and its negative sampling technique. Our work is the first to propose differentially-private learning with skip-grams. In addition, we devise data grouping techniques within the skip-gram framework that pool together trajectories from multiple users in order to accelerate learning and improve model accuracy. Experiments conducted on real datasets demonstrate that our approach significantly boosts prediction accuracy compared to existing DP-SGD techniques. 

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5. Differentially-Private Next-Location Prediction with Neural Networks

   Ahuja, Ritesh ; Ghinita, Gabriel ; Shahabi, Cyrus ( March 2020 , Advances in database technology)

   The emergence of mobile apps (e.g., location-based services,geo-social networks, ride-sharing) led to the collection of vast amounts of trajectory data that greatly benefit the understanding of individual mobility. One problem of particular interest is next-location prediction, which facilitates location-based advertising, point-of-interest recommendation, traffic optimization,etc. However, using individual trajectories to build prediction models introduces serious privacy concerns, since exact whereabouts of users can disclose sensitive information such as their health status or lifestyle choices. Several research efforts focused on privacy-preserving next-location prediction, but they have serious limitations: some use outdated privacy models (e.g., k-anonymity), while others employ learning models with limited expressivity (e.g., matrix factorization). More recent approaches(e.g., DP-SGD) integrate the powerful differential privacy model with neural networks, but they provide only generic and difficult-to-tune methods that do not perform well on location data, which is inherently skewed and sparse.We propose a technique that builds upon DP-SGD, but adapts it for the requirements of next-location prediction. We focus on user-level privacy, a strong privacy guarantee that protects users regardless of how much data they contribute. Central toour approach is the use of the skip-gram model, and its negative sampling technique. Our work is the first to propose differentially-private learning with skip-grams. In addition, we devise data grouping techniques within the skip-gram framework that pool together trajectories from multiple users in order to acceleratelearning and improve model accuracy. Experiments conducted on real datasets demonstrate that our approach significantly boosts prediction accuracy compared to existing DP-SGD techniques. 

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