Fully Homomorphic Encryption (FHE) is a key technology enabling privacy-preserving
computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to …

We present SimplePIR, the fastest single-server private information retrieval scheme known
to date. SimplePIR's security holds under the learning-with-errors assumption. To answer a …

Consider a cloud server that owns a key-value store and provides a private query service to
its clients. Preserving client privacy in this setting is difficult because the key-value store is …

Cross-silo federated learning (FL) leverages homomorphic encryption (HE) to obscure the
model updates from the clients. However, HE poses the challenges of complex …

The recent huge advance of Large Language Models (LLMs) is mainly driven by the
increase in the number of parameters. This has led to substantial memory capacity …

With the rapid growth of classification scale in deep learning systems, the final classification
layer becomes extreme classification with a memory footprint exceeding the main memory …

Many relational data in our daily life are represented as graphs, making graph application
an important workload. Because of the large scale of graph datasets, moving graph data to …

On-device machine learning (ML) inference can enable the use of private user data on user
devices without revealing them to remote servers. However, a pure on-device solution to …

Training recommendation systems (RecSys) faces several challenges as it requires the
“data preprocessing” stage to preprocess an ample amount of raw data and feed them to the …

Fully Homomorphic Encryption (FHE) is a technique that allows arbitrary computations to be
performed on encrypted data without the need for decryption, making it ideal for securing …