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Irippuge Milinda Perera

Irippuge Milinda Perera

Irippuge Milinda Perera is a Software Engineer in the Security & Privacy group at Google. He received his Ph.D. in Computer Science from the City University of New York (CUNY) in 2015. His research interests are in data anonymization, mobile security, steganography, and cryptography.
Authored Publications
Google Publications
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    A General Purpose Transpiler for Fully Homomorphic Encryption
    Shruthi Gorantala
    Rob Springer
    Sean Purser-Haskell
    Asra Ali
    Eric P. Astor
    Itai Zukerman
    Sam Ruth
    Phillipp Schoppmann
    Sasha Kulankhina
    Alain Forget
    David Marn
    Cameron Tew
    Rafael Misoczki
    Bernat Guillen
    Xinyu Ye
    Damien Desfontaines
    Aishe Krishnamurthy
    Miguel Guevara
    Yurii Sushko
    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
    Google COVID-19 Vaccination Search Insights: Anonymization Process Description
    Adam Boulanger
    Akim Kumok
    Arti Patankar
    Benjamin Miller
    Chaitanya Kamath
    Charlotte Stanton
    Chris Scott
    Damien Desfontaines
    Evgeniy Gabrilovich
    Gregory A. Wellenius
    John S. Davis
    Karen Lee Smith
    Krishna Kumar Gadepalli
    Mark Young
    Shailesh Bavadekar
    Tague Griffith
    Yael Mayer
    Arxiv.org (2021)
    Preview abstract This report describes the aggregation and anonymization process applied to the COVID-19 Vaccination Search Insights~\cite{vaccination}, a publicly available dataset showing aggregated and anonymized trends in Google searches related to COVID-19 vaccination. The applied anonymization techniques protect every user’s daily search activity related to COVID-19 vaccinations with $(\varepsilon, \delta)$-differential privacy for $\varepsilon = 2.19$ and $\delta = 10^{-5}$. View details
    Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description
    Akim Kumok
    Chaitanya Kamath
    Charlotte Stanton
    Damien Desfontaines
    Evgeniy Gabrilovich
    Gerardo Flores
    Gregory Alexander Wellenius
    Ilya Eckstein
    John S. Davis
    Katie Everett
    Krishna Kumar Gadepalli
    Rayman Huang
    Shailesh Bavadekar
    Thomas Ludwig Roessler
    Venky Ramachandran
    Yael Mayer
    Arxiv.org, N/A (2020)
    Preview abstract This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset, a publicly available dataset that shows aggregated, anonymized trends in Google searches for symptoms (and some related topics). The anonymization process is designed to protect the daily search activity of every user with \varepsilon-differential privacy for \varepsilon = 1.68. View details
    KHyperLogLog: Estimating Reidentifiability and Joinability of Large Data at Scale
    Pern Hui Chia
    Damien Desfontaines
    Daniel Simmons-Marengo
    Chao Li
    Wei-Yen Day
    Qiushi Wang
    Miguel Guevara
    Proceedings of the 2019 IEEE Symposium on Security and Privacy
    Preview abstract Understanding the privacy relevant characteristics of data sets, such as reidentifiability and joinability, is crucial for data governance, yet can be difficult for large data sets. While computing the data characteristics by brute force is straightforward, the scale of systems and data collected by large organizations demands an efficient approach. We present KHyperLogLog (KHLL), an algorithm based on approximate counting techniques that can estimate the reidentifiability and joinability risks of very large databases using linear runtime and minimal memory. KHLL enables one to measure reidentifiability of data quantitatively, rather than based on expert judgement or manual reviews. Meanwhile, joinability analysis using KHLL helps ensure the separation of pseudonymous and identified data sets. We describe how organizations can use KHLL to improve protection of user privacy. The efficiency of KHLL allows one to schedule periodic analyses that detect any deviations from the expected risks over time as a regression test for privacy. We validate the performance and accuracy of KHLL through experiments using proprietary and publicly available data sets View details
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