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In order to deliver high performance in cloud computing, we generally exploit and leverage RDMA (Remote Direct Memory Access) in networking and NVM (Non-Volatile Memory) in end systems. Due to no involvement of CPU, one-sided RDMA becomes…

Quantum computers create new security risks for today's encryption systems. This paper presents an improved version of the Advanced Encryption Standard (AES) that uses quantum technology to strengthen protection. Our approach offers two…

Persistent memory (PMEM) devices present an opportunity to retain the flexibility of main memory data structures and algorithms, but augment them with reliability and persistence. The challenge in doing this is to combine replication (for…

Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train or infer with private data while exploiting accelerators, such as GPUs, that are hosted in the cloud.…

Predictable execution time upon accessing shared memories in multi-core real-time systems is a stringent requirement. A plethora of existing works focus on the analysis of Double Data Rate Dynamic Random Access Memories (DDR DRAMs), or…

As organizations struggle with processing vast amounts of information, outsourcing sensitive data to third parties becomes a necessity. To protect the data, various cryptographic techniques are used in outsourced database systems to ensure…

Searchable encryption (SE) is one of the key enablers for building encrypted databases. It allows a cloud server to search over encrypted data without decryption. Dynamic SE additionally includes data addition and deletion operations to…

Neural radiance fields (NeRF) have transformed 3D reconstruction and rendering, facilitating photorealistic image synthesis from sparse viewpoints. This work introduces an explicit data reuse neural rendering (EDR-NR) architecture, which…

Emerging non-volatile memory (NVM) is promising for building future HPC. Leveraging the non-volatility of NVM as main memory, we can restart the application using data objects remaining on NVM when the application crashes. This paper…

A quantum network distributes quantum entanglements between remote nodes, and is key to many applications in secure communication, quantum sensing and distributed quantum computing. This paper explores the fundamental trade-off between the…

Bounding privacy leakage over compositions, i.e., privacy accounting, is a key challenge in differential privacy (DP). The privacy parameter ($\eps$ or $\delta$) is often easy to estimate but hard to bound. In this paper, we propose a new…

The use of trusted hardware has become a promising solution to enable privacy-preserving machine learning. In particular, users can upload their private data and models to a hardware-enforced trusted execution environment (e.g. an enclave…

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…

We propose a privacy-preserving ensemble infused enhanced Deep Neural Network (DNN) based learning framework in this paper for Internet-of-Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, edge…

The vigorous development of the Internet has spurred exponential data growth, yet data is predominantly stored in isolated user entities, hampering its full value realization. In large-scale deployment of ``AI+industries'' such as smart…

As edge devices gain stronger computing power, deploying high-performance DNN models on untrusted hardware has become a practical approach to cut inference latency and protect user data privacy. Given high model training costs and user…

Virtualization has become more important since cloud computing is getting more and more popular than before. There is an increasing demand for security among the cloud customers. AMD plans to provide Secure Encrypted Virtualization (SEV)…

The widespread adoption of convolutional neural networks (CNNs) in resource-constrained scenarios has driven the development of Machine Learning as a Service (MLaaS) system. However, this approach is susceptible to privacy leakage, as the…

Recent advances in secure hardware technologies, such as Intel SGX or ARM TrustZone, offer an opportunity to substantially reduce the costs of Byzantine fault-tolerance by placing the program code and state within a secure enclave known as…

Encrypted behavioral patterns provide a unique avenue for classifying complex digital threats without reliance on explicit feature extraction, enabling detection frameworks to remain effective even when conventional static and behavioral…