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Securing tomorrow: a comprehensive survey on the synergy of Artificial Intelligence and information security
This survey paper explores the transformative role of Artificial Intelligence (AI) in information
security. Traditional methods, especially rule-based approaches, faced significant …
security. Traditional methods, especially rule-based approaches, faced significant …
Bolt: Privacy-preserving, accurate and efficient inference for transformers
The advent of transformers has brought about significant advancements in traditional
machine learning tasks. However, their pervasive deployment has raised concerns about …
machine learning tasks. However, their pervasive deployment has raised concerns about …
FAB: An FPGA-based accelerator for bootstrappable fully homomorphic encryption
Fully Homomorphic Encryption (FHE) offers protection to private data on third-party cloud
servers by allowing computations on the data in encrypted form. To support general-purpose …
servers by allowing computations on the data in encrypted form. To support general-purpose …
SHARP: A short-word hierarchical accelerator for robust and practical fully homomorphic encryption
Fully homomorphic encryption (FHE) is an emerging cryptographic technology that
guarantees the privacy of sensitive user data by enabling direct computations on encrypted …
guarantees the privacy of sensitive user data by enabling direct computations on encrypted …
Ark: Fully homomorphic encryption accelerator with runtime data generation and inter-operation key reuse
Homomorphic Encryption (HE) is one of the most promising post-quantum cryptographic
schemes that enable privacy-preserving computation on servers. However, noise …
schemes that enable privacy-preserving computation on servers. However, noise …
Sok: Fully homomorphic encryption accelerators
Fully Homomorphic Encryption (FHE) is a key technology enabling privacy-preserving
computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to …
computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to …
Tensorfhe: Achieving practical computation on encrypted data using gpgpu
In the cloud computing era, privacy protection is becoming pervasive in a broad range of
applications (eg, machine learning, data mining, etc). Fully Homomorphic Encryption (FHE) …
applications (eg, machine learning, data mining, etc). Fully Homomorphic Encryption (FHE) …
Poseidon: Practical homomorphic encryption accelerator
With the development of the important solution for privacy computing, the explosion of data
size and computing intensity in Fully Homomorphic Encryption (FHE) has brought enormous …
size and computing intensity in Fully Homomorphic Encryption (FHE) has brought enormous …
Honeycomb: Secure and efficient {GPU} executions via static validation
Graphics Processing Units (GPUs) unlock emerging use cases like large language models
and autonomous driving. They process a large amount of sensitive data, where security is of …
and autonomous driving. They process a large amount of sensitive data, where security is of …
FPT: A fixed-point accelerator for torus fully homomorphic encryption
Fully Homomorphic Encryption (FHE) is a technique that allows computation on encrypted
data. It has the potential to drastically change privacy considerations in the cloud, but high …
data. It has the potential to drastically change privacy considerations in the cloud, but high …