PIR with compressed queries and amortized query processing
Private information retrieval (PIR) is a key building block in many privacy-preserving
systems. Unfortunately, existing constructions remain very expensive. This paper introduces …
systems. Unfortunately, existing constructions remain very expensive. This paper introduces …
Faster cryptonets: Leveraging sparsity for real-world encrypted inference
Homomorphic encryption enables arbitrary computation over data while it remains
encrypted. This privacy-preserving feature is attractive for machine learning, but requires …
encrypted. This privacy-preserving feature is attractive for machine learning, but requires …
A framework for collaborative learning in secure high-dimensional space
As the amount of data generated by the Internet of the Things (IoT) devices keeps
increasing, many applications need to offload computation to the cloud. However, it often …
increasing, many applications need to offload computation to the cloud. However, it often …
Ensemble method for privacy-preserving logistic regression based on homomorphic encryption
Homomorphic encryption (HE) is one of promising cryptographic candidates resolving
privacy issues in machine learning on sensitive data such as biomedical data and financial …
privacy issues in machine learning on sensitive data such as biomedical data and financial …
Accelerating fourier and number theoretic transforms using tensor cores and warp shuffles
S Durrani, MS Chughtai, M Hidayetoglu… - 2021 30th …, 2021 - ieeexplore.ieee.org
The discrete Fourier transform (DFT) and its specialized case, the number theoretic
transform (NTT), are two important mathematical tools having applications in several areas …
transform (NTT), are two important mathematical tools having applications in several areas …
Accelerating finite-field and torus fhe via compute-enabled (s) ram
Fully Homomorphic Encryption (FHE) allows outsourced computation on clients' encrypted
data while preserving data privacy. FHE's high computational intensity incurs high overhead …
data while preserving data privacy. FHE's high computational intensity incurs high overhead …
Algorithmic acceleration of b/fv-like somewhat homomorphic encryption for compute-enabled ram
Abstract Somewhat Homomorphic Encryption (SHE) allows arbitrary computation with finite
multiplicative depths to be performed on encrypted data, but its overhead is high due to …
multiplicative depths to be performed on encrypted data, but its overhead is high due to …
TERSE: tiny encryptions and really speedy execution for post-quantum private stream aggregation
The massive scale and performance demands of privacy-preserving data aggregation make
integration of security and privacy difficult. Traditional tools in private computing are not well …
integration of security and privacy difficult. Traditional tools in private computing are not well …
Accelerating Homomorphic Comparison Operations for Thresholding Using an Asymmetric Input Range and Input Scaling
In a cyber-physical system (CPS), the interconnection of cyber and physical components
occurs through a network. This structure, particularly cyber components and networks …
occurs through a network. This structure, particularly cyber components and networks …
HEKWS: Privacy-Preserving convolutional neural network-based keyword spotting with a ciphertext packing technique
DL Elworth, S Kim - 2022 IEEE 24th International Workshop on …, 2022 - ieeexplore.ieee.org
Keyword spotting (KWS) is a key technology in smart devices. However, privacy issues in
these devices have been constantly raised. To solve this problem, this paper applies …
these devices have been constantly raised. To solve this problem, this paper applies …