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1. Devils in Your Apps: Vulnerabilities and User Privacy Exposure in Mobile Notification Systems

   Jiadong Lou, Xiaohan Zhang (June 2023, Proceedings of 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2023))

   Witnessing the blooming adoption of push notifications on mobile devices, this new message delivery paradigm has become pervasive in diverse applications. Accompanying with its broad adoption, the potential security risks and privacy exposure issues raise public concerns regarding its great social impacts. This paper conducts the first attempt to exploit the mobile notification ecosystem. By dissecting its structural elements and implementation process, a comprehensive vulnerability analysis is conducted towards the complete flow of mobile notification from platform enrollment to messaging. Meanwhile, for privacy exposure, we first examine the implementation of privacy policy compliance by proposing a three-level inspection approach to guide our analysis. Then, our top-down methods from documentation analysis, application network traffic study, to static analysis expose the illicit data collection behaviors in released applications. In addition, we uncover the potential privacy inference resulted from the notification monitoring. To support our analysis, we conduct empirical studies on 12 most popular notification platforms and perform static analysis over 30,000+ applications. We discover: 1) six platforms either provide ambiguous KEY naming rules or offer vulnerable messaging APIs; 2) privacy policy compliance implementations are either stagnated at the documentation stages (8 of 12 platforms) or never implemented in apps, resulting in billions of users suffering from privacy exposure; and 3) some apps can stealthily monitor notification messages delivering to other apps, potentially incurring user privacy inference risks. Our study raises the urgent demand for better regulations of mobile notification deployment. 

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2. When Push Comes to Ads: Measuring the Rise of (Malicious) Push Advertising

   https://doi.org/10.1145/3419394.3423631  

   Subramani, Karthika; Yuan, Xingzi; Setayeshfar, Omid; Vadrevu, Phani; Lee, Kyu Hyung; Perdisci, Roberto (October 2020, Proceedings of the 2020 ACM Internet Measurement Conference (IMC))

   null (Ed.)

   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 yet been 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 URL filters were mostly unable to block them, thus leaving a significant abuse vector unchecked. 

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3. DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy

   https://doi.org/10.1145/3448016.3457306  

   Wang, Chenghong; Bater, Johes; Nayak, Kartik; Machanavajjhala, Ashwin (June 2021, SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data)

   In this paper, we consider privacy-preserving update strategies for secure outsourced growing databases. Such databases allow appendonly data updates on the outsourced data structure while analysis is ongoing. Despite a plethora of solutions to securely outsource database computation, existing techniques do not consider the information that can be leaked via update patterns. To address this problem, we design a novel secure outsourced database framework for growing data, DP-Sync, which interoperate with a large class of existing encrypted databases and supports efficient updates while providing differentially-private guarantees for any single update. We demonstrate DP-Sync's practical feasibility in terms of performance and accuracy with extensive empirical evaluations on real world datasets. 

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4. On The Ridiculousness of Notice and Consent: Contradictions in App Privacy Policies

   Okoyomon, Ehimare; Samarin, Nikita; Wijesekera, Primal; Elazari Bar On, Amit; Vallina-Rodriguez, Narseo; Reyes, Irwin; Feal, Álvaro; Egelman, Serge (June 2019, The Workshop on Technology and Consumer Protection (ConPro ’19))

   The dominant privacy framework of the information age relies on notions of “notice and consent.” That is, service providers will disclose, often through privacy policies, their data collection practices, and users can then consent to their terms. However, it is unlikely that most users comprehend these disclosures, which is due in no small part to ambiguous, deceptive, and misleading statements. By comparing actual collection and sharing practices to disclosures in privacy policies, we demonstrate the scope of the problem. Through analysis of 68,051 apps from the Google Play Store, their corresponding privacy policies, and observed data transmissions, we investigated the potential misrepresentations of apps in the Designed For Families (DFF) program, inconsistencies in disclosures regarding third-party data sharing, as well as contradictory disclosures about secure data transmissions. We find that of the 8,030 DFF apps (i.e., apps directed at children), 9.1% claim that their apps are not directed at children, while 30.6% claim to have no knowledge that the received data comes from children. In addition, we observe that 10.5% of 68,051 apps share personal identifiers with third-party service providers, yet do not declare any in their privacy policies, and only 22.2% of the apps explicitly name third parties. This ultimately makes it not only difficult, but in most cases impossible, for users to establish where their personal data is being processed. Furthermore, we find that 9,424 apps do not use TLS when transmitting personal identifiers, yet 28.4% of these apps claim to take measures to secure data transfer. Ultimately, these divergences between disclosures and actual app behaviors illustrate the ridiculousness of the notice and consent framework. 

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5. PrivateBus: Privacy Identification and Protection in Large-Scale Bus WiFi Systems

   https://doi.org/10.1145/3380990  

   Fang, Zhihan; Fu, Boyang; Qin, Zhou; Zhang, Fan; Zhang, Desheng (March 2020, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Recently, the ubiquity of mobile devices leads to an increasing demand of public network services, e.g., WiFi hot spots. As a part of this trend, modern transportation systems are equipped with public WiFi devices to provide Internet access for passengers as people spend a large amount of time on public transportation in their daily life. However, one of the key issues in public WiFi spots is the privacy concern due to its open access nature. Existing works either studied location privacy risk in human traces or privacy leakage in private networks such as cellular networks based on the data from cellular carriers. To the best of our knowledge, none of these work has been focused on bus WiFi privacy based on large-scale real-world data. In this paper, to explore the privacy risk in bus WiFi systems, we focus on two key questions how likely bus WiFi users can be uniquely re-identified if partial usage information is leaked and how we can protect users from the leaked information. To understand the above questions, we conduct a case study in a large-scale bus WiFi system, which contains 20 million connection records and 78 million location records from 770 thousand bus WiFi users during a two-month period. Technically, we design two models for our uniqueness analyses and protection, i.e., a PB-FIND model to identify the probability a user can be uniquely re-identified from leaked information; a PB-HIDE model to protect users from potentially leaked information. Specifically, we systematically measure the user uniqueness on users' finger traces (i.e., connection URL and domain), foot traces (i.e., locations), and hybrid traces (i.e., both finger and foot traces). Our measurement results reveal (i) 97.8% users can be uniquely re-identified by 4 random domain records of their finger traces and 96.2% users can be uniquely re-identified by 5 random locations on buses; (ii) 98.1% users can be uniquely re-identified by only 2 random records if both their connection records and locations are leaked to attackers. Moreover, the evaluation results show our PB-HIDE algorithm protects more than 95% users from the potentially leaked information by inserting only 1.5% synthetic records in the original dataset to preserve their data utility. 

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