5 citations · 13 across the 29 of their papers we have counts for
38 papers
Function Tables for Secure Distributed Matrix Multiplication
Rafael G. L. D'Oliveira, Giulia Gaggero, Arturo Jaramillo Gil +3
We introduce function tables, an entrywise representation of the coefficient functions that appear in the worker responses of a secure distributed matrix multiplication (SDMM) sche…
Improved Degree Tables for Secure Distributed Matrix Multiplication
John Byrne, Rafael G. L. D'Oliveira, Michael Tait
In secure distributed matrix multiplication, a user wishes to compute the product of two matrices with the assistance of servers, in such a way that any colluding servers learn…
A Monotone Circuit Construction for Individually-Secure Multi-Secret Sharing
Cailyn Bass, Alejandro Cohen, Rafael G. L. D'Oliveira +1
In this work, we introduce a new technique for taking a single-secret sharing scheme with a general access structure and transforming it into an individually secure multi-secret sh…
Secure Distributed Matrix Multiplication with Precomputation
Ryann Cartor, Rafael G. L. D'Oliveira, Salim El Rouayheb +3
We consider the problem of secure distributed matrix multiplication in which a user wishes to compute the product of two matrices with the assistance of honest but curious servers.…
Leveraging AES Padding: dBs for Nothing and FEC for Free in IoT Systems
Jongchan Woo, Vipindev Adat Vasudevan, Benjamin D. Kim +5
The Internet of Things (IoT) represents a significant advancement in digital technology, with its rapidly growing network of interconnected devices. This expansion, however, brings…
Error Correction Capabilities of Non-Linear Cryptographic Hash Functions
Alejandro Cohen, Rafael G. L. D'Oliveira
Linear hashes are known to possess error-correcting capabilities. However, in most applications, non-linear hashes with pseudorandom outputs are utilized instead. It has also been…
Optimal Binary Differential Privacy via Graphs
Sahel Torkamani, Javad B. Ebrahimi, Parastoo Sadeghi +2
We present the notion of \emph{reasonable utility} for binary mechanisms, which applies to all utility functions in the literature. This notion induces a partial ordering on the pe…
CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation
Benjamin D. Kim, Vipindev Adat Vasudevan, Jongchan Woo +4
The use of Mutual Information (MI) as a measure to evaluate the efficiency of cryptosystems has an extensive history. However, estimating MI between unknown random variables in a h…
Generalized Rainbow Differential Privacy
Yuzhou Gu, Ziqi Zhou, Onur Günlü +4
We study a new framework for designing differentially private (DP) mechanisms via randomized graph colorings, called rainbow differential privacy. In this framework, datasets are n…
CERMET: Coding for Energy Reduction with Multiple Encryption Techniques --
Jongchan Woo, Vipindev Adat Vasudevan, Benjamin Kim +4
This paper presents CERMET, an energy-efficient hardware architecture designed for hardware-constrained cryptosystems. CERMET employs a base cryptosystem in conjunction with networ…
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks
Natalie Lang, Nir Shlezinger, Rafael G. L. D'Oliveira +1
Federated learning (FL) is an emerging paradigm that allows a central server to train machine learning models using remote users' data. Despite its growing popularity, FL faces cha…
A Non-Asymptotic Analysis of Mismatched Guesswork
Alexander Mariona, Homa Esfahanizadeh, Rafael G. L. D'Oliveira +1
The problem of mismatched guesswork considers the additional cost incurred by using a guessing function which is optimal for a distribution when the random variable to be guess…
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Homa Esfahanizadeh, Adam Yala, Rafael G. L. D'Oliveira +8
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice. A promising technique…
A Bivariate Invariance Principle
Alexander Mariona, Homa Esfahanizadeh, Rafael G. L. D'Oliveira +1
A notable result from analysis of Boolean functions is the Basic Invariance Principle (BIP), a quantitative nonlinear generalization of the Central Limit Theorem for multilinear po…
Absolute Security in High-Frequency Wireless Links
Alejandro Cohen, Rafael G. L. D'Oliveira, Chia-Yi Yeh +6
Security against eavesdropping is one of the key concerns in the design of any communication system. Many common considerations of the security of a wireless communication channel…
Wideband Time Frequency Coding
Kathleen Yang, Salman Salamatian, Rafael G. L . D'Oliveira +1
In the wideband regime, the performance of many of the popular modulation schemes such as code division multiple access and orthogonal frequency division multiplexing falls quickly…
Heterogeneous Differential Privacy via Graphs
Sahel Torkamani, Javad B. Ebrahimi, Parastoo Sadeghi +2
We generalize a previous framework for designing utility-optimal differentially private (DP) mechanisms via graphs, where datasets are vertices in the graph and edges represent dat…
AES as Error Correction: Cryptosystems for Reliable Communication
Alejandro Cohen, Rafael G. L. D'Oliveira, Ken R. Duffy +2
In this paper, we show that the Advanced Encryption Standard (AES) cryptosystem can be used as an error-correcting code to obtain reliability over noisy communication and data syst…
Partial Encryption after Encoding for Security and Reliability in Data Systems
Alejandro Cohen, Rafael G. L. D'Oliveira, Ken R. Duffy +1
We consider the problem of secure and reliable communication over a noisy multipath network. Previous work considering a noiseless version of our problem proposed a hybrid universa…
Rainbow Differential Privacy
Ziqi Zhou, Onur Günlü, Rafael G. L. D'Oliveira +3
We extend a previous framework for designing differentially private (DP) mechanisms via randomized graph colorings that was restricted to binary functions, corresponding to colorin…
Syfer: Neural Obfuscation for Private Data Release
Adam Yala, Victor Quach, Homa Esfahanizadeh +5
Balancing privacy and predictive utility remains a central challenge for machine learning in healthcare. In this paper, we develop Syfer, a neural obfuscation method to protect aga…
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…
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…
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…