Privacy-Preserving Analytics, Processing and Data Management (original) (raw)

Application of homomorphic encryption in machine learning

Yulliwas Ameur

View PDFchevron_right

Privacy-Preserving Homomorphic Encryption Schemes for Machine Learning in the Cloud

Ranadeep Reddy Palle

ESP Journal of Engineering & Technology Advancements, 2021

View PDFchevron_right

Practical Privacy-Preserving Data Science With Homomorphic Encryption: An Overview

michela iezzi

2020 IEEE International Conference on Big Data (Big Data)

View PDFchevron_right

Privacy-Preserving Technologies for Trusted Data Spaces

Luis Muñoz-González

Technologies and Applications for Big Data Value

View PDFchevron_right

On the Trade-Offs of Combining Multiple Secure Processing Primitives for Data Analytics

João Paulo

Distributed Applications and Interoperable Systems, 2020

View PDFchevron_right

Confidential Training and Inference using Secure Multi-Party Computation on Vertically Partitioned Dataset

Jossy P GEORGE

Scalable Computing: Practice and Experience

View PDFchevron_right

Privacy-Preserving Machine Learning: Need, Methods, And Research Trends

Rana Pir

INTERNATIONAL JOURNAL OF CURRENT SCIENCE, 2022

View PDFchevron_right

Secure and non-interactive k-NN classifier using symmetric fully homomorphic encryption

Yulliwas Ameur

View PDFchevron_right

Efficient Secure Building Blocks With Application to Privacy Preserving Machine Learning Algorithms

Artrim Kjamilji

IEEE Access, 2021

View PDFchevron_right

Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities

Andrei Tchernykh

Peer-to-Peer Networking and Applications

View PDFchevron_right

Secure Multi-Party Computation for Collaborative Data Analysis

Reda Salama

E3S Web of Conferences

View PDFchevron_right

Exploring Homomorphic Encryption and Differential Privacy Techniques towards Secure Federated Learning Paradigm

Rezak Aziz

Future Internet

View PDFchevron_right

UNIQUE SOFTWARE ENGINEERING TECHNIQUES: PANACEA FOR THREAT COMPLEXITIES IN SECURE MULTIPARTY COMPUTATION (MPC) WITH BIG DATA

Computer Science & Information Technology (CS & IT) Computer Science Conference Proceedings (CSCP)

View PDFchevron_right

Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms

Rafik Hamza

Entropy

View PDFchevron_right

Privacy-Preserving Big Data Analytics: From Theory to Practice

Mohammad G. Raeini

2019

View PDFchevron_right

Privacy-Preserving Techniques for Trustworthy Data Sharing: Opportunities and Challenges for Future Research

Hosea A. Ofe

Springer eBooks, 2022

View PDFchevron_right

Privacy-Preserving Machine Learning Techniques, Challenges And Research Directions

Deval Parikh

International Research Journal of Engineering and Technology, 2024

View PDFchevron_right

A Homomorphic Encryption Approach to Implementing Two- Party Privacy Preserving Data Mining

'Yinka Oyerinde, PhD

View PDFchevron_right

Secure Multi-Party Computation for Machine Learning: A Survey

Farzad Tofigh

IEEE access, 2024

View PDFchevron_right

Secure Federated Learning with a Homomorphic Encryption Model

Mohammad Aljanabi

International Journal Papier Advance and Scientific Review

View PDFchevron_right

Big data analytics over encrypted datasets with seabed

Abhishek Modi

Operating Systems Design and Implementation, 2016

View PDFchevron_right

Privacy-Preserving Classification and Clustering Using Secure Multi-Party Computation

Saeed Samet

2000

View PDFchevron_right

Privacy-preserving Data clustering in Cloud Computing based on Fully Homomorphic Encryption

Mark Reynolds

2017

View PDFchevron_right

Advanced Cryptographic Protocols Using Homomorphic Encryption

EZE V A L H Y G I N U S UDOKA

Journal of Engineering, Technology & Applied Science, 2024

View PDFchevron_right

Privacy-Preserving Machine Learning: Methods, Challenges and Directions

James Joshi

arXiv (Cornell University), 2021

View PDFchevron_right

IRJET- Secure Data Mining in Cloud using Homomorphic Encryption

IRJET Journal

IRJET, 2020

View PDFchevron_right

FLASH: Fast and Robust Framework for Privacy-preserving Machine Learning

Megha Byali

Proceedings on Privacy Enhancing Technologies, 2020

View PDFchevron_right

Towards Using Homomorphic Encryption for Cryptographic Access Control in Outsourced Data Processing

Stefan Rass

2016

View PDFchevron_right

Secure Machine Learning over Relational Data

Qiyao Luo

arXiv (Cornell University), 2021

View PDFchevron_right

Classification of Multiparty Outsourced Data with Privacy Preservation

Avinash Thube

2016

View PDFchevron_right

Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective

Duy Kha Đinh

IEEE Access, 2021

View PDFchevron_right

Scotch: An Efficient Secure Computation Framework for Secure Aggregation

Yash More

ArXiv, 2022

View PDFchevron_right

The Next Frontier of Security: Homomorphic Encryption in Action

IJRASET Publication

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2024

View PDFchevron_right