Talks and presentations
GlucOS: Security, correctness, and simplicity for automated insulin delivery
MIT CSAIL Security Seminar 2024-2025, MIT Cambridge MA 02139 on Dec 12 2024
We present GlucOS, a novel system for trustworthy automated insulin delivery. Fundamentally, this paper is about a system we designed, implemented, and deployed on real humans and the lessons learned from our experiences. GlucOS combines algorithmic security, driver security, and end-to-end verification to protect against malicious ML models, vulnerable pump drivers, and drastic changes in human physiology. We use formal methods to prove correctness of critical components and incorporate humans as part of our defensive strategy. Our evaluation includes both a real-world deployment with seven individuals and results from simulation to show that our techniques generalize. Our results show that GlucOS maintains safety and improves glucose control even under attack conditions. This work demonstrates the potential for secure, personalized, automated healthcare systems.
GlucOS: Security, correctness, and simplicity for automated insulin delivery
Computer Systems Seminar, UC Davis, Davis CA 95616on Oct 18 2024
Type 1 Diabetes (T1D) is a metabolic disorder where an individual’s pancreas stops producing insulin. To compensate, they inject synthetic insulin. Computer systems, called automated insulin delivery systems, exist that inject insulin automatically. However, insulin is a dangerous hormone, where too much insulin can kill people in a matter of hours and too little insulin can kill people in a matter of days. In this paper, we take on the challenge of building a new trustworthy automated insulin delivery system, called GlucOS. In our design, we apply separation principles to keep our implementation simple, we use formal methods to prove correct the most critical parts of the system, and we design novel security mechanisms and policies to withstand malicious components and attacks on the system. We report on real world use for one individual for 6 months using GlucOS. Our data shows that for this individual, our MLbased algorithm runs safely and manages their T1D effectively. We also run our system on 21 virtual humans using simulations and show that our security and safety mechanisms enable ML to improve their core T1D measures of metabolic health by 4.3% on average. Finally, we show that our security and safety mechanisms maintain recommended levels of control over T1D even in the face of active attacks that would have otherwise led to death.
Aragorn: A Privacy-Enhancing System for Mobile Cameras
UbiComp’24, Sofitel Melbourne on Collins, Melbourne VIC 3000 Australia on Oct 7 2024
Mobile app developers often rely on cameras to implement rich features. However, giving apps unfettered access to the mobile camera poses a privacy threat when camera frames capture sensitive information that is not needed for the app’s functionality. To mitigate this threat, we present Aragorn, a novel privacy-enhancing mobile camera system that provides fine grained control over what information can be present in camera frames before apps can access them. Aragorn automatically sanitizes camera frames by detecting regions that are essential to an app’s functionality and blocking out everything else to protect privacy while retaining app utility. Aragorn can cater to a wide range of camera apps and incorporates knowledge distillation and crowdsourcing to extend robust support to previously unsupported apps. In our evaluations, we see that, with no degradation in utility, Aragorn detects credit cards with 89% accuracy and faces with 100% accuracy in context of credit card scanning and face recognition respectively. We show that Aragorn’s implementation in the Android camera subsystem only suffers an average drop of 0.01 frames per second in frame rate. Our evaluations show that the overhead incurred by Aragorn to system performance is reasonable.
GlucOS: Security, correctness, and simplicity for automated insulin delivery
FairComp Workshop @ UbiComp 2024, Sofitel Melbourne on Collins, Melbourne VIC 3000 Australia on Oct 5 2024
Type 1 Diabetes (T1D) is a metabolic disorder where an individual’s pancreas stops producing insulin. To compensate, they inject synthetic insulin. Computer systems, called automated insulin delivery systems, exist that inject insulin automatically. However, insulin is a dangerous hormone, where too much insulin can kill people in a matter of hours and too little insulin can kill people in a matter of days. In this paper, we take on the challenge of building a new trustworthy automated insulin delivery system, called GlucOS. In our design, we apply separation principles to keep our implementation simple, we use formal methods to prove correct the most critical parts of the system, and we design novel security mechanisms and policies to withstand malicious components and attacks on the system. We report on real world use for one individual for 6 months using GlucOS. Our data shows that for this individual, our MLbased algorithm runs safely and manages their T1D effectively. We also run our system on 21 virtual humans using simulations and show that our security and safety mechanisms enable ML to improve their core T1D measures of metabolic health by 4.3% on average. Finally, we show that our security and safety mechanisms maintain recommended levels of control over T1D even in the face of active attacks that would have otherwise led to death.
FP-Rowhammer: Rowhammer-Based Device Fingerprinting
UC Davis, Davis, CA 95616 on Feb 16 2024
Device fingerprinting relies on attributes that capture heterogeneity in hardware and software configurations to extract unique and stable fingerprints. Fingerprinting countermeasures attempt to either present a uniform fingerprint across different devices through normalization or present different fingerprints for the same device each time through obfuscation. We present FP-Rowhammer, a Rowhammer-based device fingerprinting approach that can build unique and stable fingerprints even across devices with normalized or obfuscated hardware and software configurations. To this end, FP-Rowhammer leverages the DRAM manufacturing process variation that gives rise to unique distributions of Rowhammer-induced bit flips across different DRAM modules.
Aragorn: An Automated and Extensible Privacy-Enhancing System for Mobile Cameras
UC Davis, Davis, CA 95616 on Jan 21 2022
Mobile app developers often rely on high-quality cameras to implement rich functionalities. However, giving apps unfettered access to the mobile camera poses a serious threat to user privacy because camera frames may contain sensitive information that is beyond the scope of the app. To mitigate this privacy concern, we present Aragorn, a novel privacy-enhancing mobile camera system that automatically sanitizes camera frames to only contain user-authorized objects without needing any manual user input.
eXeGAN: Not all Malware is created Equal
Blue Hexagon, Sunnyvale, CA 94086 on Sep 20 2019
Antivirus engines use static analysis of executables to detect malware. In some cases, they prefer static analysis over dynamic analysis since dynamic analysis is time consuming and can be unreliable if the malware detects that it is being executed in a sandboxed environment for analysis. The advent of machine learning has greatly shifted malware detection in favor of these engines, since ML models tend to generalize, helping them detect completely new and unseen types of malware. We explore if malware authors can also leverage machine learning to evade detection by presenting eXeGAN, a generative adversarial network that mutates executables to evade ML and ruled based static detection, while preserving their functionality.