69 citations · 707 across the 60 of their papers we have counts for
5 papers · 1 filter
Evaluating the Potential of In-Memory Processing to Accelerate Homomorphic Encryption
Mpoki Mwaisela, Joel Hari, Peterson Yuhala +3
The widespread adoption of cloud-based solutions introduces privacy and security concerns. Techniques such as homomorphic encryption (HE) mitigate this problem by allowing computat…
On the Cost of Model-Serving Frameworks: An Experimental Evaluation
Pasquale De Rosa, Yérom-David Bromberg, Pascal Felber +2
In machine learning (ML), the inference phase is the process of applying pre-trained models to new, unseen data with the objective of making predictions. During the inference phase…
BlindexTEE: A Blind Index Approach towards TEE-supported End-to-end Encrypted DBMS
Louis Vialar, Jämes Ménétrey, Valerio Schiavoni +1
Using cloud-based applications comes with privacy implications, as the end-user looses control over their data. While encrypting all data on the client is possible, it largely redu…
CryptoAnalytics: Cryptocoins Price Forecasting with Machine Learning Techniques
Pasquale De Rosa, Pascal Felber, Valerio Schiavoni
This paper introduces CryptoAnalytics, a software toolkit for cryptocoins price forecasting with machine learning (ML) techniques. Cryptocoins are tradable digital assets exchanged…
Practical Forecasting of Cryptocoins Timeseries using Correlation Patterns
Pasquale De Rosa, Pascal Felber, Valerio Schiavoni
Cryptocoins (i.e., Bitcoin, Ether, Litecoin) are tradable digital assets. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption tec…