What a lovely hat

Private Set Intersection (PSI) is a protocol where two parties with individually held confidential sets want to jointly learn (or secret-share) the intersection of these two sets and reveal nothing else to each other. In this paper, we introduce a natural extension of this notion to approximate matching. Specifically, given a distance metric between elements, an approximate PSI (Approx-PSI) allows to run PSI where ``close'' elements match. Assuming that elements are either ``close'' or...

Computing the distance between two non-normalized vectors $\mathbfit{x}$ and $\mathbfit{y}$, represented by $\Delta(\mathbfit{x},\mathbfit{y})$ and comparing it to a predefined public threshold $\tau$ is an essential functionality used in privacy-sensitive applications such as biometric authentication, identification, machine learning algorithms ({\em e.g.,} linear regression, k-nearest neighbors, etc.), and typo-tolerant password-based authentication. Tackling a widely used distance...

Within just four years, the blockchain-based Decentralized Finance (DeFi) ecosystem has accumulated a peak total value locked (TVL) of more than 253 billion USD. This surge in DeFi’s popularity has, unfortunately, been accompanied by many impactful incidents. According to our data, users, liquidity providers, speculators, and protocol operators suffered a total loss of at least 3.24 billion USD from Apr 30, 2018 to Apr 30, 2022. Given the blockchain’s transparency and increasing incident...

Nearest neighbor search is a fundamental building-block for a wide range of applications. A privacy-preserving protocol for nearest neighbor search involves a set of clients who send queries to a remote database. Each client retrieves the nearest neighbor(s) to its query in the database without revealing any information about the query. To ensure database privacy, clients must learn as little as possible beyond the query answer, even if behaving maliciously by deviating from...

In the last decade, machine learning-based side-channel attacks have become a standard option when investigating profiling side-channel attacks. At the same time, the previous state-of-the-art technique, template attack, started losing its importance and was more considered a baseline to compare against. As such, most of the results reported that machine learning (and especially deep learning) could significantly outperform the template attack. Nevertheless, the template attack still has...

In Near-Neighbor Search (NNS), a client queries a database (held by a server) for the most similar data (near-neighbors) given a certain similarity metric. The Privacy-Preserving variant (PP-NNS) requires that neither server nor the client shall learn information about the other party’s data except what can be inferred from the outcome of NNS. The overwhelming growth in the size of current datasets and the lack of a truly secure server in the online world render the existing solutions...

Privacy-preserving collaborative data analysis enables richer models than what each party can learn with their own data. Secure Multi-Party Computation (MPC) offers a robust cryptographic approach to this problem, and in fact several protocols have been proposed for various data analysis and machine learning tasks. In this work, we focus on secure similarity computation between text documents, and the application to $k$-nearest neighbors (\knn) classification. Due to its non-parametric...

Thumbnail preserving encryption (TPE) was suggested by Wright et al. as a way to balance privacy and usability for online image sharing. The idea is to encrypt a plaintext image into a ciphertext image that has roughly the same thumbnail as well as retaining the original image format. At the same time, TPE allows users to take advantage of much of the functionality of online photo management tools, while still providing some level of privacy against the service provider. In this work we...