108 citations · 238 across the 19 of their papers we have counts for
20 papers
CryptDough: A Unified Analytics Engine for Secure Multiparty Computation
Muhammad Faisal, Alessandra Lanz, Sam Buxbaum +4
We present CryptDough, a unified analytics engine for secure multiparty computation (MPC). CryptDough enables multiple distrusting parties to jointly execute a data analysis pipeli…
Making Sense of Private Advertising: A Principled Approach to a Complex Ecosystem
Kyle Hogan, Alishah Chator, Gabriel Kaptchuk +2
In this work, we model the end-to-end pipeline of the advertising ecosystem, allowing us to identify two main issues with the current trajectory of private advertising proposals. F…
ORQ: Complex Analytics on Private Data with Strong Security Guarantees
Eli Baum, Sam Buxbaum, Nitin Mathai +4
We present ORQ, a system that enables collaborative analysis of large private datasets using cryptographically secure multi-party computation (MPC). ORQ protects data against semi-…
Can the Government Compel Decryption? Don't Trust -- Verify
Aloni Cohen, Sarah Scheffler, Mayank Varia
If a court knows that a respondent knows the password to a device, can the court compel the respondent to enter that password into the device? In this work, we propose a new approa…
Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity
Sarah Scheffler, Eran Tromer, Mayank Varia
A central notion in U.S. copyright law is judging the substantial similarity between an original and an (allegedly) derived work. Capturing this notion has proven elusive, and the…
Secrecy: Secure collaborative analytics on secret-shared data
John Liagouris, Vasiliki Kalavri, Muhammad Faisal +1
We present a relational MPC framework for secure collaborative analytics on private data with no information leakage. Our work targets challenging use cases where data owners may n…
Secret Sharing MPC on FPGAs in the Datacenter
Pierre-Francois Wolfe, Rushi Patel, Robert Munafo +2
Multi-Party Computation (MPC) is a technique enabling data from several sources to be used in a secure computation revealing only the result while protecting the original data, fac…
Anonymous Collocation Discovery: Harnessing Privacy to Tame the Coronavirus
Ran Canetti, Ari Trachtenberg, Mayank Varia
Successful containment of the Coronavirus pandemic rests on the ability to quickly and reliably identify those who have been in close proximity to a contagious individual. Existing…
Case Study: Disclosure of Indirect Device Fingerprinting in Privacy Policies
Julissa Milligan, Sarah Scheffler, Andrew Sellars +3
Recent developments in online tracking make it harder for individuals to detect and block trackers. Some sites have deployed indirect tracking methods, which attempt to uniquely id…
Conclave: secure multi-party computation on big data (extended TR)
Nikolaj Volgushev, Malte Schwarzkopf, Ben Getchell +3
Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint computations without revealing private data. Current MPC algorithms scale poorly with data siz…
Revealing the Unseen: How to Expose Cloud Usage While Protecting User Privacy
Ata Turk, Mayank Varia, Georgios Kellaris
Cloud users have little visibility into the performance characteristics and utilization of the physical machines underpinning the virtualized cloud resources they use. This uncerta…
Privacy with Estimation Guarantees
Hao Wang, Lisa Vo, Flavio P. Calmon +3
We study the central problem in data privacy: how to share data with an analyst while providing both privacy and utility guarantees to the user that owns the data. In this setting,…
Principal Inertia Components and Applications
Flavio P. Calmon, Ali Makhdoumi, Muriel Médard +3
We explore properties and applications of the Principal Inertia Components (PICs) between two discrete random variables and . The PICs lie in the intersection of information…
SoK: Cryptographically Protected Database Search
Benjamin Fuller, Mayank Varia, Arkady Yerukhimovich +6
Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator…
Parallel Vectorized Algebraic AES in MATLAB for Rapid Prototyping of Encrypted Sensor Processing Algorithms and Database Analytics
Jeremy Kepner, Vijay Gadepally, Braden Hancock +3
The increasing use of networked sensor systems and networked databases has led to an increased interest in incorporating encryption directly into sensor algorithms and database ana…
Computing on Masked Data to improve the Security of Big Data
Vijay Gadepally, Braden Hancock, Benjamin Kaiser +4
Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed…
Hiding Symbols and Functions: New Metrics and Constructions for Information-Theoretic Security
Flavio du Pin Calmon, Muriel Médard, Mayank Varia +3
We present information-theoretic definitions and results for analyzing symmetric-key encryption schemes beyond the perfect secrecy regime, i.e. when perfect secrecy is not attained…
Computing on Masked Data: a High Performance Method for Improving Big Data Veracity
Jeremy Kepner, Vijay Gadepally, Pete Michaleas +4
The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of…
An Exploration of the Role of Principal Inertia Components in Information Theory
Flavio du Pin Calmon, Mayank Varia, Muriel Médard
The principal inertia components of the joint distribution of two random variables and are inherently connected to how an observation of is statistically related to a h…
Bounds on inference
Flavio du Pin Calmon, Mayank Varia, Muriel Médard +3
Lower bounds for the average probability of error of estimating a hidden variable X given an observation of a correlated random variable Y, and Fano's inequality in particular, pla…