Related papers: Adding Query Privacy to Robust DHTs
In distributed networks, calculating the maximum element is a fundamental task in data analysis, known as the distributed maximum consensus problem. However, the sensitive nature of the data involved makes privacy protection essential.…
The security of future large-scale IoT networks is critically threatened by the ``Harvest Now, Decrypt Later'' (HNDL) attack paradigm. Securing the massive, long-lived data streams from these systems requires protocols that are both…
Cross-attention has emerged as a cornerstone module in modern artificial intelligence, underpinning critical applications such as retrieval-augmented generation (RAG), system prompting, and guided stable diffusion. However, this is a rising…
In this short note we want to introduce {\em anonymous oblivious transfer} a new cryptographic primitive which can be proven to be strictly more powerful than oblivious transfer. We show that all functions can be robustly realized by multi…
When sharing sensitive relational databases with other parties, a database owner aims to (i) have privacy guarantees for the database entries, (ii) have liability guarantees (via fingerprinting) in case of unauthorized sharing of its…
In the \emph{shuffle model} of differential privacy, data-holding users send randomized messages to a secure shuffler, the shuffler permutes the messages, and the resulting collection of messages must be differentially private with regard…
Quantum oblivious transfer (QOT) is an essential cryptographic primitive. But unconditionally secure QOT is known to be impossible. Here we propose a practical QOT protocol, which is perfectly secure against dishonest sender without relying…
The Domain Name System (DNS), which converts domain names to their respective IP addresses, has advanced enhancements aimed at safeguarding DNS data and users' identity from attackers. The recent privacy-focused advancements have enabled…
Privacy leaks are an unfortunate and an integral part of the current Internet domain name resolution. Each DNS query generated by a user reveals -- to one or more DNS servers -- the origin and target of that query. Over time, a user's…
Many applications that benefit from data offload to cloud services operate on private data. A now-long line of work has shown that, even when data is offloaded in an encrypted form, an adversary can learn sensitive information by analyzing…
Unencrypted DNS traffic between users and DNS resolvers can lead to privacy and security concerns. In response to these privacy risks, many browser vendors have deployed DNS-over-HTTPS (DoH) to encrypt queries between users and DNS…
We consider the standard $K$-armed bandit problem under a distributed trust model of differential privacy (DP), which enables to guarantee privacy without a trustworthy server. Under this trust model, previous work largely focus on…
Recently, opportunities to transmit speech data to deep learning models executed in the cloud have increased. This has led to growing concerns about speech privacy, including both speaker-specific information and the linguistic content of…
Commitment schemes are essential to many cryptographic protocols and schemes with applications that include privacy-preserving computation on data, privacy-preserving authentication, and, in particular, oblivious transfer protocols. For…
With the development of machine learning, it is difficult for a single server to process all the data. So machine learning tasks need to be spread across multiple servers, turning the centralized machine learning into a distributed one.…
In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these…
Blockchains provide environments where parties can interact transparently and securely peer-to-peer without needing a trusted third party. Parties can trust the integrity and correctness of transactions and the verifiable execution of…
Cryptography's importance in our everyday lives continues to grow in our increasingly digital world. Oblivious transfer (OT) has long been a fundamental and important cryptographic primitive since it is known that general two-party…
Differential privacy is a robust privacy standard that has been successfully applied to a range of data analysis tasks. Despite much recent work, optimal strategies for answering a collection of correlated queries are not known. We study…
Federated Learning (FL) solutions with central Differential Privacy (DP) have seen large improvements in their utility in recent years arising from the matrix mechanism, while FL solutions with distributed (more private) DP have lagged…