Many privacy-enhancing technologies, in particular anonymous communication networks (ACNs) as a key building block, suffer from a lack of a sufficient number of participants. Without high user participation, ACNs are vulnerable to traffic analysis attacks. The only ACN with a high number of participants (around 1.5 million users) is Tor. Yet, Tor is prone to traffic analysis attacks traffic pattern attacks. While other ACNs have been proposed that are even secure against global attackers, they are not scalable and suffer from a low number of participants, since even a perfect ACN can at most hide a user among all participating users. These ACNs are in a vicious circle: the lack of participants leads to low degree of anonymity, and a low degree of anonymity makes these ACNs unattractive for users. In this work, we break this vicious cycle by studying the question: Can an anonymous communication network be strengthened by "forced" participation? What privacy guarantees and performance can such an ACN provide? We develop CoverUp, a system that "forces" visitors of highly accessed websites (entry servers) to become involuntary participants of an ACN. CoverUp triggers users to participate in a centralized, constant-rate mix by leveraging basic functionality of their browsers to execute (JavaScript) code served by the entry servers. Candidates for entry servers could be universities or news sites. They would let a distinct CoverUp server provide (via an iframe) JavaScript code to the end-users' browsers, which in turn makes them participate in the ACN via a mix server. Visitors of these entry servers' websites become (involuntary) participants of an ACN, creating cover traffic for voluntary participants. For voluntary participants, we developed a browser extension that renders their CoverUp requests indistinguishable from the cover traffic of involuntary participants.

Mobile application spoofing is an attack where a malicious mobile app mimics the visual appearance of another one. A common example of mobile application spoofing is a phishing attack where the adversary tricks the user into revealing her password to a malicious app that resembles the legitimate one. In this paper, we propose a novel spoofing detection approach, tailored to the protection of mobile app login screens, using screenshot extraction and visual similarity comparison. We use deception rate as a novel similarity metric for measuring how likely the user is to consider a potential spoofing app as one of the protected applications. We conducted a large-scale online study where participants evaluated spoofing samples of popular mobile app login screens, and used the study results to implement a detection system that accurately estimates deception rate. We show that efficient detection is possible with low overhead.

We show that the new hover (floating touch) technology, available in a number of today's smartphone models, can be abused by malicious Android applications to record all touchscreen input into applications system-wide. Leveraging this attack, a malicious application running on the system is able to capture sensitive input such as passwords and PINs, record all user's social interactions, as well as profile user's behavior. To evaluate our attack we implemented Hoover, a proof-of-concept malicious application that runs in the background and records all input to all foreground applications. We evaluated Hoover with 20 users, across two different Android devices and two input methods, stylus and finger. In the case of touchscreen input by finger, Hoover estimated the positions of users' clicks within an error of 100 pixels and keyboard input with an accuracy of 79%. Hoover captured users' input by stylus even more accurately, estimating users' clicks within 2 pixels and keyboard input with an accuracy of 98%. Differently from existing well-known side channel attacks, this is the first work that proves the security implications of the hover technology and its potential to steal all user inputs with high granularity. We discuss ways of mitigating this attack and show that this cannot be done by simply restricting access to permissions or imposing additional cognitive load on the users since this would significantly constrain the intended use of the hover technology.

Recent incidents of data-breaches from the cloud suggest that users should not trust the cloud provider to enforce access control on their data. We focus on mitigating trust to the cloud in scenarios where granting access to data not only considers user identities (as in conventional access policies), but also contextual information such as the user's location and time of access. Previous work in this context assumes a fully trusted cloud that is further capable of locating users. We introduce LoTAC, a novel framework that seamlessly integrates the operation of a cloud provider and a localization infrastructure to enforce location- and time-based access control to cloud-stored data. In LoTAC, the two entities operate independently and are only trusted to offer their basic services: the cloud provider is used and trusted only to reliably store data, the localization infrastructure is used and trusted only to accurately locate users. Furthermore, neither the cloud provider nor the localization infrastructure can access the data, even if they collude. LoTAC protocols require no changes to the cloud provider and minimal changes to the localization infrastructure. We evaluate our protocols using a cellular network as the localization infrastructure and show that they incur in low communication and computation costs and scale well with a large number of users and policies.

Wireless communication provides unique security challenges, but also enables novel ways to defend against attacks. In the past few years, a number of works discussed the use of friendly jamming to protect the confidentiality of the communicated data as well as to enable message authentication and access control. In this work, we analytically and experimentally evaluate the confidentiality that can be achieved by the use of friendly jamming, given an attacker with multiple receiving antennas. We construct a MIMO-based attack that allows the attacker to recover data protected by friendly jamming and refine the conditions for which this attack is most effective. Our attack shows that friendly jamming cannot provide strong confidentiality guarantees in all settings. We further test our attack in a setting where friendly jamming is used to protect the communication to medical implants.