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Private heavy-hitters is a data-collection task where multiple clients possess private bit strings, and data-collection servers aim to identify the most popular strings without learning anything about the clients' inputs. In this work, we introduce PLASMA: a private analytics framework in the three-server setting that protects the privacy of honest clients and the correctness of the protocol against a coalition of malicious clients and a malicious server. Our core primitives are a verifiable incremental distributed point function (VIDPF) and a batched consistency check, which are of independent interest. Our VIDPF introduces new methods to validate client inputs based on hashing. Meanwhile, our batched consistency check uses Merkle trees to validate multiple client sessions together in a batch. This drastically reduces server communication across multiple client sessions, resulting in significantly less communication compared to related works. Finally, we compare PLASMA with the recent works of Asharov et al. (CCS'22) and Poplar (S&P'21) and compare in terms of monetary cost for different input sizes. 

The California Consumer Privacy Act and other privacy laws give people a right to opt out of the sale and sharing of personal information. In combination with privacy preference signals, especially, Global Privacy Control (GPC), such rights have the potential to empower people to assert control over their data. However, many laws prohibit opt out settings being turned on by default. The resulting usability challenges for people to exercise their rights motivate generalizable active privacy choice — an interface design principle to make opt out settings usable without defaults. It is based on the idea of generalizing one individual opt out choice towards a larger set of choices. For example, people may apply an opt out choice on one site towards a larger set of sites. We explore generalizable active privacy choice in the context of GPC. We design and implement nine privacy choice schemes in a browser extension and explore them in a usability study with 410 participants. We find that generalizability features tend to decrease opt out utility slightly. However, at the same time, they increase opt out efficiency and make opting out less disruptive, which was more important to most participants. For the least disruptive scheme, selecting website categories to opt out from, 98% of participants expressed not feeling disrupted, a 40% point increase over the baseline schemes. 83% of participants understood the meaning of GPC. They also made their opt out choices with intent and, thus, in a legally relevant manner. To help people exercise their opt out rights via GPC our results support the adoption of a generalizable active privacy choice interface in web browsers. 

This work presents RPM, a scalable anonymous communication protocol suite using secure multiparty computation (MPC) with the offline-online model. We generate random, unknown permutation matrices in a secret-shared fashion and achieve improved (online) performance and the lightest communication and computation overhead for the clients compared to the state of art robust anonymous communication protocols. Using square-lattice shuffling, we make our protocol scale well as the number of clients increases. We provide three protocol variants, each targeting different input volumes and MPC frameworks/libraries. Besides, due to the modular design, our protocols can be easily generalized to support more MPC functionalities and security properties as they get developed. We also illustrate how to generalize our protocols to support two-way anonymous communication and secure sorting. We have implemented our protocols using the MP-SPDZ library suit and the benchmark illustrates that our protocols achieve unprecedented online phase performance with practical offline phases.