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The Android OS has become the most popular mobile operating system leading to a significant increase in the spread of Android malware. Consequently, several static and dynamic analysis systems have been developed to detect Android malware.…
Safeguarding the Intellectual Property (IP) of data has become critically important as machine learning applications continue to proliferate, and their success heavily relies on the quality of training data. While various mechanisms exist…
Self-supervised learning (SSL) is increasingly attractive for pre-training encoders without requiring labeled data. Downstream tasks built on top of those pre-trained encoders can achieve nearly state-of-the-art performance. The pre-trained…
End-users seldom read verbose privacy policies, leading app stores like Google Play to mandate simplified data safety declarations as a user-friendly alternative. However, these self-declared disclosures often contradict the full privacy…
Context: Digital and physical trails of user activities are collected over the use of software applications and systems. As software becomes ubiquitous, protecting user privacy has become challenging. With the increase of user privacy…
In this thesis we consider the problem of information hiding in the scenarios of interactive systems, statistical disclosure control, and refinement of specifications. We apply quantitative approaches to information flow in the first two…
In the last years the pervasive use of sensors, as they exist in smart devices, e.g., phones, watches, medical devices, has increased dramatically the availability of personal data. However, existing research on data collection primarily…
Training data privacy has been a top concern in AI modeling. While methods like differentiated private learning allow data contributors to quantify acceptable privacy loss, model utility is often significantly damaged. In practice,…
Digital and physical footprints are a trail of user activities collected over the use of software applications and systems. As software becomes ubiquitous, protecting user privacy has become challenging. With the increase of user privacy…
Google has mandated developers to use Data Safety Sections (DSS) to increase transparency in data collection and sharing practices. In this paper, we present a comprehensive analysis of Google's Data Safety Section (DSS) using both…
In the digitized world, smartphones and their apps play an important role. To name just a few examples, some apps offer possibilities for entertainment, others for online banking, and others offer support for two-factor authentication.…
Deep learning is a powerful weapon to boost application performance in many fields, including face recognition, object detection, image classification, natural language understanding, and recommendation system. With the rapid increase in…
The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of cryptographic misuses, a comprehensive and accurate…
Many existing Artificial Intelligence (AI) solutions on mobile devices rely on an extensive collection of sensitive data, raising privacy concerns and often requiring storage for both context and model improvement. Apple's Private Cloud…
Mobile gaming apps increasingly rely on third-party Software Development Kits SDKs for advertising, analytics, attribution, and user engagement, potentially introducing privacy exposure beyond traditional permission based risks. Existing…
Fragmentation is a serious problem in the Android ecosystem. This problem is mainly caused by the fast evolution of the system itself and the various customizations independently maintained by different smartphone manufacturers. Many…
There is a known tension between the need to analyze personal data to drive business and privacy concerns. Many data protection regulations, including the EU General Data Protection Regulation (GDPR) and the California Consumer Protection…
We present Anadroid, a static malware analysis framework for Android apps. Anadroid exploits two techniques to soundly raise precision: (1) it uses a pushdown system to precisely model dynamically dispatched interprocedural and…
In recent years, major privacy laws like the GDPR have brought about positive changes. However, challenges remain in enforcing the laws, particularly due to under-resourced regulators facing a large number of potential privacy-violating…
Document understanding models have recently demonstrated remarkable performance by leveraging extensive collections of user documents. However, since documents often contain large amounts of personal data, their usage can pose a threat to…