Monte Carlo execution time estimation for Privacy-preserving Distributed Function Evaluation protocols

04/03/2021
by   Stefano M P C Souza, et al.
0

Recent developments in Machine Learning and Deep Learning depend heavily on cloud computing and specialized hardware, such as GPUs and TPUs. This forces those using those models to trust private data to cloud servers. Such scenario has prompted a large interest on Homomorphic Cryptography and Secure Multi-Party Computation protocols that allow the use of cloud computing power in a privacy-preserving manner. When comparing the efficiency of such protocols, most works in literature resort to complexity analysis that gives asymptotic higher-bounding limits of computational cost when input size tends to infinite. These limits may be very different from the actual cost or execution time, when performing such computations over small, or average-sized datasets. We argue that Monte Carlo methods can render better computational cost and time estimates, fostering better design and implementation decisions for complex systems, such as Privacy-Preserving Machine Learning Frameworks.

READ FULL TEXT
research
01/12/2023

Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networks

In recent years, with the development of cloud computing platforms, priv...
research
04/23/2022

Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

Privacy-Preserving Cloud Computing is an emerging technology with many a...
research
07/15/2020

Cloud-based Privacy-Preserving Collaborative Consumption for Sharing Economy

Cloud computing has been a dominant paradigm for a variety of informatio...
research
10/04/2019

PINFER: Privacy-Preserving Inference for Machine Learning

The foreseen growing role of outsourced machine learning services is rai...
research
11/08/2021

Privacy Guarantees for Cloud-based State Estimation using Partially Homomorphic Encryption

The privacy aspect of state estimation algorithms has been drawing high ...
research
09/22/2020

Privacy Preserving K-Means Clustering: A Secure Multi-Party Computation Approach

Knowledge discovery is one of the main goals of Artificial Intelligence....
research
04/22/2022

Towards Privacy-Preserving Neural Architecture Search

Machine learning promotes the continuous development of signal processin...

Please sign up or login with your details

Forgot password? Click here to reset