More Like this

1. 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)

   Abstract 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. 

   more » « less
2. Your Phone is My Proxy: Detecting and Understanding Mobile Proxy Networks

   https://doi.org/10.14722/ndss.2021.24008  

   Mi, Xianghang; Tang, Siyuan; Li, Zhengyi; Liao, Xiaojing; Qian, Feng; Wang, XiaoFeng (January 2021, Proceeding of ISOC Network and Distributed System Security Symposium (NDSS), 2021)

   null (Ed.)

   Residential proxy has emerged as a service gaining popularity recently, in which proxy providers relay their customers’ network traffic through millions of proxy peers under their control. We find that many of these proxy peers are mobile devices, whose role in the proxy network can have significant security implications since mobile devices tend to be privacy and resource-sensitive. However, little effort has been made so far to understand the extent of their involvement, not to mention how these devices are recruited by the proxy network and what security and privacy risks they may pose. In this paper, we report the first measurement study on the mobile proxy ecosystem. Our study was made possible by a novel measurement infrastructure, which enabled us to identify proxy providers, to discover proxy SDKs (software development kits), to detect Android proxy apps built upon the proxy SDKs, to harvest proxy IP addresses, and to understand proxy traffic. The information collected through this infrastructure has brought to us new understandings of this ecosystem and important security discoveries. More specifically, 4 proxy providers were found to offer app developers mobile proxy SDKs as a competitive app monetization channel, with $50K per month per 1M MAU (monthly active users). 1,701 Android APKs (belonging to 963 Android apps) turn out to have integrated those proxy SDKs, with most of them available on Google Play with at least 300M installations in total. Furthermore, 48.43% of these APKs are flagged by at least 5 anti-virus engines as malicious, which could explain why 86.60% of the 963 Android apps have been removed from Google Play by Oct 2019. Besides, while these apps display user consent dialogs on traffic relay, our user study indicates that the user consent texts are quite confusing. We even discover a proxy SDK that stealthily relays traffic without showing any notifications. We also captured 625K cellular proxy IPs, along with a set of suspicious activities observed in proxy traffic such as ads fraud. We have reported our findings to affected parties, offered suggestions, and proposed the methodologies to detect proxy apps and proxy traffic. 

   more » « less
3. It Takes a Village: A Case for Including Extended Family Members in the Joint Oversight of Family-based Privacy and Security for Mobile Smartphones

   https://doi.org/10.1145/3544549.3585904  

   Akter, Mamtaj; Alghamdi, Leena; Kropczynski, Jess; Lipford, Heather Richter; Wisniewski, Pamela J. (April 2023, ACM)

   We conducted a user study with 19 parent-teen dyads to understand the perceived benefits and drawbacks of using a mobile app that allows them to co-manage mobile privacy, safety, and security within their families. While the primary goal of the study was to understand the use case as it pertained to parents and teens, an emerging finding from our study was that participants found value in extending app use to other family members (siblings, cousins, and grandparents). Participants felt that it would help bring the necessary expertise into their immediate family network and help protect the older adults and children of the family from privacy and security risks. However, participants expressed that co-monitoring by extended family members might cause tensions in their families, creating interpersonal conflicts. To alleviate these concerns, participants suggested more control over the privacy features to facilitate sharing their installed apps with only trusted family members. 

   more » « less
4. Augmented reality for high-throughput phenotyping

   Wu, Shanshan; Neilsen, Mitchell L. (July 2019, 17th International Conference on Scientific Computing)

   Augmented Reality (AR) enables elements of a computer-generated digital world to be integrated with a user’s perception of the physical world. Smart glasses, like smart phones, have independent operating systems and they can support a variety of different applications and modes of communication to support augmented reality. This paper details the development of a novel new application that extends a widely-used mobile app for phenotyping and allows agronomists to interact with the app while keeping their hands free to perform field work. The smart glasses accept voice commands from the user and communicate with the mobile phone app via Bluetooth. In addition, changes detected by the mobile phone are displayed to the user on the smart glasses. This enables agronomists to efficiently collect phenotypic data. 

   more » « less
5. Task-optimized User Clustering based on Mobile App Usage for Cold-start Recommendations

   Liu, Bulou; Bai, Bing; Xie, Weibang; Guo, Yiwen; Chen, Hao (August 2022, ACM SIGKDD Conference on Knowledge Discovery and Data Mining)

   This paper reports our recent practice of recommending articles to cold-start users at Tencent. Transferring knowledge from information-rich domains to help user modeling is an effective way to address the user-side cold-start problem. Our previous work demonstrated that general-purpose user embeddings based on mobile app usage helped article recommendations. However, high-dimensional embeddings are cumbersome for online usage, thus limiting the adoption. On the other hand, user clustering, which partitions users into several groups, can provide a lightweight, online-friendly, and explainable way to help recommendations. Effective user clustering for article recommendations based on mobile app usage faces unique challenges, including (1) the gap between an active user’s behavior of mobile app usage and article reading, and (2) the gap between mobile app usage patterns of active and cold-start users. To address the challenges, we propose a tailored Dual Alignment User Clustering (DAUC) model, which applies a sample-wise contrastive alignment to liminate the gap between active users’ mobile app usage and article reading behavior, and a distribution-wise adversarial alignment to eliminate the gap between active users’ and cold-start users’ app usage behavior. With DAUC, cold-start recommendation-optimized user clustering based on mobile app usage can be achieved. On top of the user clusters, we further build candidate generation strategies, real-time features, and corresponding ranking models without much engineering difficulty. Both online and offline experiments demonstrate the effectiveness of our work. 

   more » « less