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Security of embedded computing systems is becoming of paramount concern as these devices become more ubiquitous, contain personal information and are increasingly used for financial transactions. Security attacks targeting embedded systems…
Embedded systems are ubiquitous. However, physical access of users and likewise attackers makes them often threatened by fault attacks: a single fault during the computation of a cryptographic primitive can lead to a total loss of system…
Run-time attacks against programs written in memory-unsafe programming languages (e.g., C and C++) remain a prominent threat against computer systems. The prevalence of techniques like return-oriented programming (ROP) in attacking…
The ongoing trend of moving data and computation to the cloud is met with concerns regarding privacy and protection of intellectual property. Cloud Service Providers (CSP) must be fully trusted to not tamper with or disclose processed data,…
As vehicles become increasingly connected and autonomous, they accumulate and manage various personal data, thereby presenting a key challenge in preserving privacy during data sharing and processing. This survey reviews applications of…
Real-world applications are now processing big-data sets, often bottlenecked by the data movement between the compute units and the main memory. Near-memory computing (NMC), a modern data-centric computational paradigm, can alleviate these…
In the non-volatile memory, ensuring the security and correctness of persistent data is fundamental. However, the security and persistence issues are usually studied independently in existing work. To achieve both data security and…
Extended Asynchronous DRAM Refresh (eADR) proposed by Intel extends the persistence domain from the Non-Volatile Memory (NVM) to CPU caches and offers the persistence guarantee. Due to allowing lazy persistence and decreasing the amounts of…
Despite decades of efforts to resolve, memory safety violations are still persistent and problematic in modern systems. Various defense mechanisms have been proposed, but their deployment in real systems remains challenging because of…
When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust between data owners and data scientists. Data owners are…
Most existing solutions for protecting VMs assume known attack patterns or signatures and focus on detecting malicious manipulations of system files and kernel level memory structures. In this research we develop a system called ferify,…
Verona is a concurrent object-oriented programming language that organises all the objects in a program into a forest of isolated regions. Memory is managed locally for each region, so programmers can control a program's memory use by…
Virtualization plays an essential role in providing security to computational systems by isolating execution environments. Many software solutions, called hypervisors, have been proposed to provide virtualization capabilities. However, only…
Leveraging parallel hardware (e.g. GPUs) for deep neural network (DNN) training brings high computing performance. However, it raises data privacy concerns as GPUs lack a trusted environment to protect the data. Trusted execution…
Confidential computing alleviates the concerns of distrustful customers by removing the cloud provider from their trusted computing base and resolves their disincentive to migrate their workloads to the cloud. This is facilitated by new…
Software-managed heterogeneous memory (HM) provides a promising solution to increase memory capacity and cost efficiency. However, to release the performance potential of HM, we face a problem of data management. Given an application with…
This paper outlines the design of `Quest-V', which is implemented as a collection of separate kernels operating together as a distributed system on a chip. Quest-V uses virtualization techniques to isolate kernels and prevent local faults…
Apart from the actual CPU, modern server motherboards contain other auxiliary components, for example voltage regulators for power management. Those are connected to the CPU and the separate Baseboard Management Controller (BMC) via the…
We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art of private deep learning…
Memory protection units (MPUs) are hardware-assisted security features that are commonly used in embedded processors such as the ARM 940T, Infineon TC1775, and Xilinx Zynq. MPUs partition the memory statically, and set individual protection…