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We present a real-world deployment of secure multiparty computation to predict political preference from private web browsing data. To estimate aggregate preferences for the 2024 U.S. presidential election candidates, we collect and analyze secret-shared data from nearly 8000 users from August 2024 through February 2025, with over 2000 daily active users sustained throughout the bulk of the survey. The use of MPC allows us to compute over sensitive web browsing data that users would otherwise be more hesitant to provide. We collect data using a custom-built Chrome browser extension and perform our analysis using the CrypTen MPC library. To our knowledge, we provide the first implementation under MPC of a model for the learning from label proportions (LLP) problem in machine learning, which allows us to train on unlabeled web browsing data using publicly available polling and election results as the ground truth. 

The SysteMPC workshop brings together cryptographers and systems researchers to discuss advances in overcom- ing practical challenges of using MPC in the wild. Topics of interest include cryptography and systems co-design, pro- gramming abstractions, modularity of cryptographic soft- ware, hardware acceleration, experimentation, and integra- tion with the existing ecosystem. In this short report, we summarize the inaugural edition of the workshop, which was held on July 10, 2025 at Boston University. 

If a web service is so secure that it does not even know---and does not want to know---the identity and contact info of its users, can it still offer account recovery if a user forgets their password? This paper is the culmination of the authors' work to design a cryptographic protocol for account recovery for use by a prominent secure matching system: a web-based service that allows survivors of sexual misconduct to become aware of other survivors harmed by the same perpetrator. In such a system, the list of account-holders must be safeguarded, even against the service provider itself. In this work, we design an account recovery system that, on the surface, appears to follow the typical workflow: the user types in their email address, receives an email containing a one-time link, and answers some security questions. Behind the scenes, the defining feature of our recovery system is that the service provider can perform email-based account validation without knowing, or being able to learn, a list of users' email addresses. Our construction uses standardized cryptography for most components, and it has been deployed in production at the secure matching system. As a building block toward our main construction, we design a new cryptographic primitive that may be of independent interest: an oblivious pseudorandom function that can either have a fully-private input or a partially-public input, and that reaches the same output either way. This primitive allows us to perform online rate limiting for account recovery attempts, without imposing a bound on the creation of new accounts. We provide an open-source implementation of this primitive and provide evaluation results showing that the end-to-end interaction time takes 8.4-60.4 ms in fully-private input mode and 3.1-41.2 ms in partially-public input mode. 

We present TVA, a multi-party computation (MPC) system for secure analytics on secret-shared time series data. TVA achieves strong security guarantees in the semi-honest and malicious settings, and high expressivity by enabling complex analytics on inputs with unordered and irregular timestamps. TVA is the first system to support arbitrary composition of oblivious window operators, keyed aggregations, and multiple filter predicates, while keeping all data attributes private, including record timestamps and user-defined values in query predicates. At the core of the TVA system lie novel protocols for secure window assignment: (i) a tumbling window protocol that groups records into fixed-length time buckets and (ii) two session window protocols that identify periods of activity followed by periods of inactivity. We also contribute a new protocol for secure division with a public divisor, which may be of independent interest. We evaluate TVA on real LAN and WAN environments and show that it can efficiently compute complex window-based analytics on inputs of 2^22 records with modest use of resources. When compared to the state-of-the-art, TVA achieves up to 5.8× lower latency in queries with multiple filters and two orders of magnitude better performance in window aggregation.