Joel Reardon

Debjyoti Mukherjee, Alireza Ahmadi, Maryam Vahdat Pour et al.

Application markets provide a communication channel between app developers and their end-users in form of app reviews, which allow users to provide feedback about the apps. Although security and privacy in mobile apps are one of the biggest issues, it is unclear how much people are aware of these or discuss them in reviews. In this study, we explore the privacy and security concerns of users using reviews in the Google Play Store. For this, we conducted a study by analyzing around 2.2M reviews from the top 539 apps of this Android market. We found that 0.5\% of these reviews are related to the security and privacy concerns of the users. We further investigated these apps by performing dynamic analysis which provided us valuable insights into their actual behaviors. Based on the different perspectives, we categorized the apps and evaluated how the different factors influence the users' perception of the apps. It was evident from the results that the number of permissions that the apps request plays a dominant role in this matter. We also found that sending out the location can affect the users' thoughts about the app. The other factors do not directly affect the privacy and security concerns for the users.

Paul-Olivier Dehaye, Joel Reardon

Proximity tracing apps have been proposed as an aide in dealing with the COVID-19 crisis. Some of those apps leverage attenuation of Bluetooth beacons from mobile devices to build a record of proximate encounters between a pair of device owners. The underlying protocols are known to suffer from false positive and re-identification attacks. We present evidence that the attacker's difficulty in mounting such attacks has been overestimated. Indeed, an attacker leveraging a moderately successful app or SDK with Bluetooth and location access can eavesdrop and interfere with these proximity tracing systems at no hardware cost and perform these attacks against users who do not have this app or SDK installed. We describe concrete examples of actors who would be in a good position to execute such attacks. We further present a novel attack, which we call a biosurveillance attack, which allows the attacker to monitor the exposure risk of a smartphone user who installs their app or SDK but who does not use any contact tracing system and may falsely believe that they have opted out of the system. Through traffic auditing with an instrumented testbed, we characterize precisely the behaviour of one such SDK that we found in a handful of apps---but installed on more than one hundred million mobile devices. Its behaviour is functionally indistinguishable from a re-identification or biosurveillance attack and capable of executing a false positive attack with minimal effort. We also discuss how easily an attacker could acquire a position conducive to such attacks, by leveraging the lax logic for granting permissions to apps in the Android framework: any app with some geolocation permission could acquire the necessary Bluetooth permission through an upgrade, without any additional user prompt. Finally we discuss motives for conducting such attacks.

Kaihua Qin, Henryk Hadass, Arthur Gervais et al.

Lightweight Bitcoin clients execute a Simple Payment Verification (SPV) protocol to verify the validity of transactions related to a particular user. Currently, lightweight clients use Bloom filters to significantly reduce the amount of bandwidth required to validate a particular transaction. This is despite the fact that research has shown that Bloom filters are insufficient at preserving the privacy of clients' queries. In this paper we describe our design of an SPV protocol that leverages Private Information Retrieval (PIR) to create fully private and performant queries. We show that our protocol has a low bandwidth and latency cost; properties that make our protocol a viable alternative for lightweight Bitcoin clients and other cryptocurrencies with a similar SPV model. In contract to Bloom filters, our PIR-based approach offers deterministic privacy to the user. Among our results, we show that in the worst case, clients who would like to verify 100 transactions occurring in the past week incurs a bandwidth cost of 33.54 MB with an associated latency of approximately 4.8 minutes, when using our protocol. The same query executed using the Bloom-filter-based SPV protocol incurs a bandwidth cost of 12.85 MB; this is a modest overhead considering the privacy guarantees it provides.

Paul-Olivier Dehaye, Joel Reardon

Ahead of the rollout of the SwissCovid contact tracing app, an official public security test was performed. During this audit, Prof. Serge Vaudenay and Dr. Martin Vuagnoux described a large set of problems with the app, including a new variation of a known false-positive attack, leveraging a cryptographic weakness in the Google and Apple Exposure Notification framework to tamper with the emitted Bluetooth beacons. Separately, the first author described a re-identification attack leveraging rogue apps or SDKs. The response from the Swiss cybersecurity agency and the Swiss public health authority was to claim these various attacks were unlikely as they required physical proximity of the attacker with the target (although it was admitted the attacker could be further than two meters). The physical presence of the attacker in Switzerland was deemed significant as it would imply such attackers would fall under the Swiss Criminal Code. We show through one example that a much larger variety of adversaries must be considered in the scenarios originally described and that these attacks can be done by adversaries without any physical presence in Switzerland. This goes directly against official findings of Swiss public authorities evaluating the risks associated with SwissCovid. To move the discussion further along, we briefly discuss the growth of the attack surface and harms with COVID-19 and SwissCovid prevalence in the population. While the focus of this article is on Switzerland, we emphasize the core technical findings and cybersecurity concerns are of relevance to many contact tracing efforts.

Primal Wijesekera, Arjun Baokar, Lynn Tsai et al.

Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.