activity
20132026
most citedSyfer: Neural Obfuscation for Private Data Release

2 citations · 8 across the 25 of their papers we have counts for

collaborators
Showing 2021Show all

6 papers · 1 filter

cs.IT2021

Post-Quantum Security for Ultra-Reliable Low-Latency Heterogeneous Networks

Rafael G. L. D'Oliveira, Alejandro Cohen, John Robinson +2

We consider the problem of post-quantum secure and ultra-reliable communication through a heterogeneous network consisting of multiple connections. Three performance metrics are co…

cs.IT2021

Field Trace Polynomial Codes for Secure Distributed Matrix Multiplication

Roberto Assis Machado, Rafael G. L. D'Oliveira, Salim El Rouayheb +1

We consider the problem of communication efficient secure distributed matrix multiplication. The previous literature has focused on reducing the number of servers as a proxy for mi…

cs.IT2021

Degree Tables for Secure Distributed Matrix Multiplication

Rafael G. L. D'Oliveira, Salim El Rouayheb, Daniel Heinlein +1

We consider the problem of secure distributed matrix multiplication (SDMM) in which a user wishes to compute the product of two matrices with the assistance of honest but curious s…

cs.IT2021

Private Multi-Group Aggregation

Carolina Naim, Rafael G. L. D'Oliveira, Salim El Rouayheb

We study the differentially private multi group aggregation (PMGA) problem. This setting involves a single server and users. Each user belongs to one of distinct groups and…

cs.CR2021★ 2 cited

NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training

Adam Yala, Homa Esfahanizadeh, Rafael G. L. D' Oliveira +6

Balancing the needs of data privacy and predictive utility is a central challenge for machine learning in healthcare. In particular, privacy concerns have led to a dearth of public…

cs.IT2021

Differential Privacy for Binary Functions via Randomized Graph Colorings

Rafael G. L. D'Oliveira, Muriel Medard, Parastoo Sadeghi

We present a framework for designing differentially private (DP) mechanisms for binary functions via a graph representation of datasets. Datasets are nodes in the graph and any two…