Related papers: SGX-MR: Regulating Dataflows for Protecting Access…
Embedded and IoT devices, largely powered by microcontroller units (MCUs), could be made more intelligent by leveraging on-device deep learning. One of the main challenges of neural network inference on an MCU is the extremely limited…
Performing machine learning tasks in mobile applications yields a challenging conflict of interest: highly sensitive client information (e.g., speech data) should remain private while also the intellectual property of service providers…
Modern user-facing latency-sensitive web services include numerous distributed, intercommunicating microservices that promise to simplify software development and operation. However, multiplexing of compute resources across microservices is…
Wireless networks that are decentralized and communicate without using existing infrastructure are known as mobile ad-hoc networks. The most common sorts of threats and attacks can affect MANETs. Therefore, it is advised to utilize…
Process-based confidential computing enclaves such as Intel SGX can be used to protect the confidentiality and integrity of workloads, without the overhead of virtualisation. However, they introduce a notable performance overhead,…
As power consumption from AI training and inference continues to increase, AI accelerators are being integrated directly into the CPU. Intel's Advanced Matrix Extensions (AMX) is one such example, debuting on the 4th generation Intel Xeon…
In this dissertation, we propose a memory and computing coordinated methodology to thoroughly exploit the characteristics and capabilities of the GPU-based heterogeneous system to effectively optimize applications' performance and privacy.…
Many applications benefit from computations over the data of multiple users while preserving confidentiality. We present a solution where multiple mutually distrusting users' data can be aggregated with an acceptable overhead, while…
The introduction of 5G and the Open Radio Access Network (O-RAN) architecture has enabled more flexible and intelligent network deployments. However, the increased complexity and openness of these architectures also introduce novel security…
Side-channel risks of Intel's SGX have recently attracted great attention. Under the spotlight is the newly discovered page-fault attack, in which an OS-level adversary induces page faults to observe the page-level access patterns of a…
The growing frequency of cyberattacks has heightened the demand for accurate and efficient threat detection systems. SIEM platforms are important for analyzing log data and detecting adversarial activities through rule-based queries, also…
Memory security and reliability are two of the major design concerns in cloud computing systems. State-of-the-art memory security-reliability co-designs (e.g. Synergy) have achieved a good balance on performance, confidentiality, integrity,…
Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG)…
Background. Life science is increasingly driven by Big Data analytics, and the MapReduce programming model has been proven successful for data-intensive analyses. However, current MapReduce frameworks offer poor support for reusing existing…
Protected-module architectures (PMAs) have been proposed to provide strong isolation guarantees, even on top of a compromised system. Unfortunately, Intel SGX -- the only publicly available high-end PMA -- has been shown to only provide…
Keystone is a trusted execution environment, based on RISC-V architecture. It divides the memory into a secure Keystone private memory and an unsecure non-Keystone memory, and allows code that lies inside the Keystone private memory to…
Almost all modern hardware, from phone SoCs to high-end servers with accelerators, contain memory translation and protection hardware like IOMMUs, firewalls, and lookup tables which make it impossible to reason about, and enforce protection…
Modern society is getting accustomed to the Internet of Things (IoT) and Cyber-Physical Systems (CPS) for a variety of applications that involves security-critical user data and information transfers. In the lower end of the spectrum, these…
As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations. A pragmatic solution comes from Trusted Execution Environments (TEEs), which…
Memory spatial errors, i.e., buffer overflow vulnerabilities, have been a well-known issue in computer security for a long time and remain one of the root causes of exploitable vulnerabilities. Most of the existing mitigation tools adopt a…