[HTML][HTML] How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing
Deep learning (DL) models for natural language processing (NLP) tasks often handle
private data, demanding protection against breaches and disclosures. Data protection laws …
private data, demanding protection against breaches and disclosures. Data protection laws …
Bts: An accelerator for bootstrappable fully homomorphic encryption
Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by
providing computation on encrypted data (ciphertexts). HE is based on noisy encryption …
providing computation on encrypted data (ciphertexts). HE is based on noisy encryption …
On the security of homomorphic encryption on approximate numbers
B Li, D Micciancio - Annual International Conference on the Theory and …, 2021 - Springer
We present passive attacks against CKKS, the homomorphic encryption scheme for
arithmetic on approximate numbers presented at Asiacrypt 2017. The attack is both …
arithmetic on approximate numbers presented at Asiacrypt 2017. The attack is both …
Practical privacy-preserving data science with homomorphic encryption: an overview
M Iezzi - 2020 IEEE International Conference on Big Data (Big …, 2020 - ieeexplore.ieee.org
Privacy has gained a growing interest nowadays due to the increasing and unmanageable
amount of produced confidential data. Concerns about the possibility of sharing data with …
amount of produced confidential data. Concerns about the possibility of sharing data with …
POSEIDON: Privacy-preserving federated neural network learning
S Sav, A Pyrgelis, JR Troncoso-Pastoriza… - arXiv preprint arXiv …, 2020 - arxiv.org
In this paper, we address the problem of privacy-preserving training and evaluation of neural
networks in an $ N $-party, federated learning setting. We propose a novel system …
networks in an $ N $-party, federated learning setting. We propose a novel system …
Over 100x faster bootstrapping in fully homomorphic encryption through memory-centric optimization with GPUs
Fully Homomorphic encryption (FHE) has been gaining in popularity as an emerging means
of enabling an unlimited number of operations in an encrypted message without decryption …
of enabling an unlimited number of operations in an encrypted message without decryption …
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 …
Optimized privacy-preserving cnn inference with fully homomorphic encryption
Inference of machine learning models with data privacy guarantees has been widely studied
as privacy concerns are getting growing attention from the community. Among others, secure …
as privacy concerns are getting growing attention from the community. Among others, secure …
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 …
Tensorfhe: Achieving practical computation on encrypted data using gpgpu
S Fan, Z Wang, W Xu, R Hou, D Meng… - … Symposium on High …, 2023 - ieeexplore.ieee.org
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) …