I am a Staff Research Scientist at Visa Research where I work on Cryptography and Secure Computation. My primary research interest is the development and applications of efficient methods for computing on encrypted data. Most notably is the notion of being able to compute on encrypted data and the area of research known as Secure Computation:

A group of distrusting parties wish to compute a function on their private data sets. The parties should learn the output of the function but no additional information about the data sets themselves without using a trusted third party.

Below is a collection of my recent blog posts and publications.


Label-PSI from Fully Homomorphic Encryption with Malicious Security

Hao Chen, Kim Laine & Peter Rindal ~ eprint/2018/787 ~ CCS’18

Private Set Intersection (PSI) allows two parties, the sender and the receiver, to compute the intersection of their private sets without revealing extra information to each other. We are interested in the unbalanced PSI setting, where (1) the receiver’s set is significantly smaller than the sender’s, and (2) the receiver (with the smaller set) has a low-power device. Also, in a Labeled PSI setting, the sender holds a label per each item in its set, and the receiver obtains the labels from the items in the intersection. We build upon the unbalanced PSI protocol of Chen, Laine, and Rindal (CCS 2017) in several ways: we add efficient support for arbitrary length items, we construct and implement an unbalanced Labeled PSI protocol with small communication complexity, and also strengthen the security model using Oblivious Pseudo-Random Function (OPRF) in a pre-processing phase. Our protocols outperform previous ones: for an intersection of 220 and 512 size sets of arbitrary length items our protocol has a total online running time of just 1 second (single thread), and a total communication cost of 4 MB. For a larger example, an intersection of 228 and 1024 size sets of arbitrary length items has an online running time of 12 seconds (multi-threaded), with less than 18~MB of total communication.

DiSE: Distributed Symmetric-key Encryption

Shashank Agrawal, Payman Mohassel, Pratyay Mukherjee & Peter Rindal ~ eprint/2018/727 ~ CCS’18

Threshold cryptography provides a mechanism for protecting secret keys by sharing them among multiple parties, who then jointly perform cryptographic operations. An attacker who corrupts upto a threshold number of parties cannot recover the secrets or violate security. Prior works in this space have mostly focused on definitions and constructions for public-key cryptography and digital signatures, and thus do not capture the security concerns and efficiency challenges of symmetric-key based applications which commonly use long-term (unprotected) master keys to protect data at rest, authenticate clients on enterprise networks, and secure data and payments on IoT devices.

We put forth the first formal treatment for distributed symmetric-key encryption, proposing new notions of correctness, privacy and authenticity in presence of malicious attackers. We provide strong and intuitive game-based definitions that are easy to understand and yield efficient constructions.

We propose a generic construction of threshold authenticated encryption based on any distributed pseudorandom function (DPRF). When instantiated with the two different DPRF constructions proposed by Naor, Pinkas and Reingold (Eurocrypt 1999) and our enhanced versions, we obtain several efficient constructions meeting different security definitions. We implement these variants and provide extensive performance comparisons. Our most efficient instantiation uses only symmetric-key primitives and achieves a throughput of upto 1 million encryptions/decryptions per seconds, or alternatively a sub-millisecond latency with upto 18 participating parties.

ABY3: A Mixed Protocol Framework for Machine Learning

Payman Mohassel & Peter Rindal ~ eprint/2018/403 ~ CCS’18

Machine learning is widely used to produce models for a range of applications and is increasingly offered as a service by major technology companies. However, the required massive data collection raises privacy concerns during both training and prediction stages.

In this paper, we design and implement a general framework for privacy-preserving machine learning and use it to obtain new solutions for training linear regression, logistic regression and neural network models. Our protocols are in a three-server model wherein data owners secret share their data among three servers who train and evaluate models on the joint data using three-party computation (3PC).

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PIR-PSI: Scaling Private Contact Discovery

Daniel Demmler, Peter Rindal, Mike Rosulek & Ni Trieu ~ eprint/2018/579 ~ PETS’18

An important initialization step in many social-networking applications is contact discovery, which allows a user of the service to identify which of its existing social contacts also use the service. Naive approaches to contact discovery reveal a user’s entire set of social/professional contacts to the service, presenting a significant tension between functionality and privacy.

In this work, we present a system for private contact discovery, in which the client learns only the intersection of its own contact list and a server’s user database, and the server learns only the (approximate) size of the client’s list. The protocol is specifically tailored to the case of a small client set and large user database. Our protocol has provable security guarantees and combines new ideas with state-of-the-art techniques from private information retrieval and private set intersection.

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Malicious-Secure Private Set Intersection via Dual Execution

Peter Rindal & Mike Rosulek ~ eprint/2017/769 ~ CCS’17

Private set intersection (PSI) allows two parties, who each hold a set of items, to compute the intersection of those sets without revealing anything about other items. Recent advances in PSI have significantly improved its performance for the case of semi-honest security, making semi-honest PSI a practical alternative to insecure methods for computing intersections. However, the semi-honest security model is not always a good fit for real-world problems.

In this work, we introduce a new PSI protocol that is secure in the presence of malicious adversaries. Our protocol is based entirely on fast symmetric-key primitives and inherits important techniques from state-of-the-art protocols in the semi-honest setting. Our novel technique to strengthen the protocol for malicious adversaries is inspired by the dual execution technique of Mohassel & Franklin (PKC 2006). Our protocol is optimized for the random-oracle model, but can also be realized (with a performance penalty) in the standard model.

We demonstrate our protocol’s practicality with a prototype implementation. To securely compute the intersection of two sets of size 220 requires only 13 seconds with our protocol, which is ∼12× faster than the previous best malicious-secure protocol (Rindal & Rosulek, Eurocrypt 2017), and only 3× slower than the best semi-honest protocol (Kolesnikov et al., CCS 2016).