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Royce J Wilson

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    A General Purpose Transpiler for Fully Homomorphic Encryption
    Shruthi Gorantala
    Rob Springer
    Sean Purser-Haskell
    William Lam
    Royce Wilson
    Asra Ali
    Eric P. Astor
    Itai Zukerman
    Sam Ruth
    Christoph Dibak
    Phillipp Schoppmann
    Sasha Kulankhina
    Alain Forget
    David Marn
    Cameron Tew
    Rafael Misoczki
    Bernat Guillen
    Xinyu Ye
    Dennis Kraft
    Damien Desfontaines
    Aishe Krishnamurthy
    Miguel Guevara
    Irippuge Milinda Perera
    Yurii Sushko
    Bryant Gipson
    Google LLC (2021)
    Preview abstract Fully homomorphic encryption (FHE) is an encryption scheme which enables computation on encrypted data without revealing the underlying data. While there have been many advances in the field of FHE, developing programs using FHE still requires expertise in cryptography. In this white paper, we present a fully homomorphic encryption transpiler that allows developers to convert high-level code (e.g., C++) that works on unencrypted data into high-level code that operates on encrypted data. Thus, our transpiler makes transformations possible on encrypted data. Our transpiler builds on Google's open-source XLS SDK (https://github.com/google/xls) and uses an off-the-shelf FHE library, TFHE (https://tfhe.github.io/tfhe/), to perform low-level FHE operations. The transpiler design is modular, which means the underlying FHE library as well as the high-level input and output languages can vary. This modularity will help accelerate FHE research by providing an easy way to compare arbitrary programs in different FHE schemes side-by-side. We hope this lays the groundwork for eventual easy adoption of FHE by software developers. As a proof-of-concept, we are releasing an experimental transpiler (https://github.com/google/fully-homomorphic-encryption/tree/main/transpiler) as open-source software. View details
    Evaluation of US State-Based Policy Interventions on Social Distancing Using Aggregated Mobility Data during the COVID-19 Pandemic
    Gregory Alexander Wellenius
    Swapnil Suresh Vispute
    Valeria Espinosa
    Alex Fabrikant
    Thomas Tsai
    Jonathan Hennessy
    Andrew Dai
    Brian Patrick Williams
    Krishna Kumar Gadepalli
    Adam Boulanger
    Adam Pearce
    Chaitanya Kamath
    Arran Schlosberg
    Catherine Bendebury
    Chinmoy Mandayam
    Charlotte Stanton
    Shailesh Bavadekar
    Christopher David Pluntke
    Damien Desfontaines
    Benjamin H. Jacobson
    Zan Armstrong
    Bryant Gipson
    Royce Wilson
    Andrew Philip Widdowson
    Katherine Chou
    Andrew Nathaniel Oplinger
    Tomer Shekel
    Ashish K. Jha
    Evgeniy Gabrilovich
    Nature Communications (2021)
    Preview abstract Social distancing has emerged as the primary mitigation strategy to combat the COVID-19 pandemic in the United States. However, large-scale evaluation of the effectiveness of social distancing policies are lacking. We used aggregated mobility data to quantify the impact of social distancing policies on observed changes in mobility. Declarations of states of emergency resulted in approximately a 10% reduction in time spent outside places of residence and an increase in visits to grocery stores and pharmacies. Subsequent implementation of ≥1 social distancing policies resulted in an additional 25% reduction in mobility in the following week. The seven states that subsequently ordered residents to shelter in place on or before March 23, 2020 observed an additional 29% reduction in time spent outside the residence. Our findings suggest that state-wide mandates are highly effective in achieving the goals of social distancing to minimize the transmission of COVID-19. View details
    Differentially Private SQL with Bounded User Contribution
    Royce Wilson
    Celia Yuxin Zhang
    William Lam
    Damien Desfontaines
    Daniel Simmons-Marengo
    Bryant Gipson
    Proceedings on Privacy Enhancing Technologies Symposium (2020) (to appear)
    Preview abstract Differential privacy (DP) provides a theoretical promise to users and analysts limiting the ability to determine a user’s contribution (if any) to the results of analysis. While there have been many theoretical explorations into the design of DP algorithms, few generically practical implementations of end-to-end DP engines exist. This paper presents a practical SQL-based engine that provides privacy guarantees with respect to groups of records, possibly spanning multiple tables, owned by a single entity. To date there has been little work to provide this type of protection for multiple rows in the same table or joins more generally. The engine utilizes a novel algorithm that evaluates query aggregations using a two-step process to enforce DP per owning entity. We limit the query sensitivity impact of joins by restricting and propagating a row-owner identifier at all steps, which allows us to limit row-owner contribution. For testing, we present a semi-decidable stochastic model-checking system, used to ensure privacy for the engine’s full range of statistical functions. This model provides stronger guarantees on privacy than existing systems with comparable accuracy. The result is a general purpose SQL engine, capable of answering typical analysis questions with little or no modification to existing queries. View details
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