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Ensuring the proper use of sensitive data in analytics under complex privacy policies is an increasingly critical challenge. Many existing approaches lack portability, verifiability, and scalability across diverse data processing frameworks. We introduce PICACHV, a novel security monitor that automatically enforces data use policies. It works on relational algebra as an abstraction for program semantics, enabling policy enforcement on query plans generated by programs during execution. This approach simplifies analysis across diverse analytical operations and supports various front-end query languages. By formalizing both data use policies and relational algebra semantics in Coq, we prove that PICACHV correctly enforces policies. PICACHV also leverages Trusted Execution Environments (TEEs) to enhance trust in runtime, providing provable policy compliance to stakeholders that the analytical tasks comply with their data use policies. We integrated PICACHV into Polars, a state-of-the-art data analytics framework, and evaluate its performance using the TPC-H benchmark. We also apply our approach to real-world use cases. Our work demonstrates the practical application of formal methods in securing data analytics, addressing key challenges. 

Diagnosing Alzheimer's Disease (AD) early and cost-effectively is crucial. Recent advancements in Large Language Models (LLMs) like ChatGPT have made accurate, affordable AD detection feasible. Yet, HIPAA compliance and the challenge of integrating these models into hospital systems limit their use. Addressing these constraints, we introduce ADetectoLocum, an open-source LLM equipped model designed for AD risk detection within hospital environments. This model evaluates AD risk through spontaneous patient speech, enhancing diagnostic processes without external data exchange. Our approach secures local deployment and significantly surpasses previous models in predictive accuracy for AD detection, especially in early-stage identification. ADetectoLocum therefore offers a reliable solution for AD diagnostics in healthcare institutions. 

Research on side-channel leaks has long been focusing on the information exposure from a single channel (memory, network traffic, power, etc.). Less studied is the risk of learning from multiple side channels related to a target activity (e.g., website visits) even when individual channels are not informative enough for an effective attack. Although the prior research made the first step on this direction, inferring the operations of foreground apps on iOS from a set of global statistics, still less clear are how to determine the maximum information leaks from all target-related side channels on a system, what can be learnt about the target from such leaks and most importantly, how to control information leaks from the whole system, not just from an individual channel. To answer these fundamental questions, we performed the first systematic study on multi-channel inference, focusing on iOS as the first step. Our research is based upon a novel attack technique, called Mischief, which given a set of potential side channels related to a target activity (e.g., foreground apps), utilizes probabilistic search to approximate an optimal subset of the channels exposing most information, as measured by Merit Score, a metric for correlation-based feature selection. On such an optimal subset, an inference attack is modeled as a multivariate time series classification problem, so the state-of-the-art deep-learning based solution, InceptionTime in particular, can be applied to achieve the best possible outcome. Mischief is found to work effectively on today's iOS (16.2), identifying foreground apps, website visits, sensitive IoT operations (e.g., opening the door) with a high confidence, even in an open-world scenario, which demonstrates that the protection Apple puts in place against the known attack is inadequate. Also importantly, this new understanding enables us to develop more comprehensive protection, which could elevate today's side-channel research from suppressing leaks from individual channels to controlling information exposure across the whole system. 

As service providers are moving to the cloud, users are forced to provision sensitive data to the cloud. Confidential computing leverages hardware Trusted Execution Environment (TEE) to protect data in use, no longer requiring users’ trust to the cloud. The emerging service model, Confidential Computing as a Service (CCaaS), is adopted by service providers to offer service similar to the Function-as-a-Serivce manner. However, privacy concerns are raised in CCaaS, especially in multi-user scenarios. CCaaS need to assure the data providers that the service does not leak their privacy to any unauthorized parties and clear their data after the service. To address such privacy concerns with security guarantees, we first formally define the security objective, Proof of Being Forgotten (PoBF), and prove under which security constraints PoBF can be satisfied. Then, these constraints serve as guidelines in the implementation of the PoBF-compliant Framework (PoCF). PoCF consists of a generic library for different hardware TEEs, CCaaS prototype enclaves, and a verifier to prove PoBF-compliance. PoCF leverages Rust’s robust type system and security features, to construct a verified state machine with privacy-preserving contracts. Last, the experiment results show that the protections introduced by PoCF incur minor runtime performance overhead.