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Android devices, handling sensitive data like call records and text messages, are prone to privacy breaches. Existing information flow tracking systems face difficulties in detecting these breaches due to two main challenges: the multi-layered Android platform using different programming languages (Java and C/C++), and the complex, event-driven execution flow of Android apps that complicates tracking, especially across these language barriers. Our system, DryJIN, addresses this by effectively tracking information flow within and across both Java and native modules. Utilizing symbolic execution for native code data flows and integrating it with Java data flows, DryJIN enhances existing static analysis techniques (Argus-SAF, JuCify, and FlowDroid) to cover previously unaddressed information flow patterns. We validated DryJIN ’s effectiveness through a comprehensive evaluation on over 168k apps, including malware and real-world apps, demonstrating its superiority over current state-of-the-art methods.
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VACCINE: Using Contextual Integrity For Data Leakage Detection
Modern enterprises rely on Data Leakage Prevention (DLP) systems to enforce privacy policies that prevent unintentional flow of sensitive information to unauthorized entities. However, these systems operate based on rule sets that are limited to syntactic analysis and therefore completely ignore the semantic relationships between participants involved in the information exchanges. For similar reasons, these systems cannot enforce complex privacy policies that require temporal reasoning about events that have previously occurred. To address these limitations, we advocate a new design methodology for DLP systems centered on the notion of Contextual Integrity (CI).We use the CI framework to abstract real-world communication exchanges into formally defined information flows where privacy policies describe sequences of admissible flows. CI allows us to decouple (1) the syntactic extraction of flows from information exchanges, and (2) the enforcement of privacy policies on these flows. We applied this approach to built VACCINE, a DLP auditing system for emails. VACCINE uses state-of-the-art techniques in natural language processing to extract flows from email text. It also provides a declarative language for describing privacy policies. These policies are automatically compiled to operational rules that the system uses for detecting data leakages. We evaluated VACCINE on the Enron email corpus and show that it improves over the state of the art both in terms of the expressivity of the policies that DLP systems can enforce as well as its precision in detecting data leakages.
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- Award ID(s):
- 1704527
- PAR ID:
- 10095711
- Date Published:
- Journal Name:
- Proceedings of the 2019 World Wide Web Conference
- Page Range / eLocation ID:
- 1702 to 1712
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3308558.3313655
