Related papers: Secure Aggregation with an Oblivious Server
We consider the federated frequency estimation problem, where each user holds a private item $X_i$ from a size-$d$ domain and a server aims to estimate the empirical frequency (i.e., histogram) of $n$ items with $n \ll d$. Without any…
Secure model aggregation across many users is a key component of federated learning systems. The state-of-the-art protocols for secure model aggregation, which are based on additive masking, require all users to quantize their model updates…
The Internet of Things (IoT) has become increasingly popular in people's daily lives. The pervasive IoT devices are encouraged to share data with each other in order to better serve the users. However, users are reluctant to share sensitive…
The problem of Oblivious RAM (ORAM) has traditionally been studied in a single-server setting, but more recently the multi-server setting has also been considered. Yet it is still unclear whether the multi-server setting has any inherent…
We study the hierarchical secure aggregation problem with groupwise keys. The problem consists of an aggregation server, $U$ relays, and $UV$ users, where each relay serves $V$ disjoint users, and each subset of $G$ users shares an…
Recent attacks on federated learning demonstrate that keeping the training data on clients' devices does not provide sufficient privacy, as the model parameters shared by clients can leak information about their training data. A 'secure…
In this paper, we investigate the transmission latency of the secure aggregation problem in a \emph{wireless} federated learning system with multiple curious servers. We propose a privacy-preserving coded aggregation scheme where the…
We show that some problems in information security can be solved without using one-way functions. The latter are usually regarded as a central concept of cryptography, but the very existence of one-way functions depends on difficult…
Motivated by the applications of secure multiparty computation as a privacy-protecting data analysis tool, and identifying oblivious transfer as one of its main practical enablers, we propose a practical realization of randomized quantum…
Secure multiparty computation enables collaborative computations across multiple users while preserving individual privacy, which has a wide range of applications in finance, machine learning and healthcare. Secure multiparty computation…
Secure aggregation is motivated by federated learning (FL) where a cloud server aims to compute an {aggregated} model (i.e., weights of deep neural networks) of the locally-trained models of numerous clients {through an iterative…
This paper introduces Flamingo, a system for secure aggregation of data across a large set of clients. In secure aggregation, a server sums up the private inputs of clients and obtains the result without learning anything about the…
Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A major bottleneck in scaling federated learning to a large…
This paper presents a secure aggregation system Armadillo that has disruptive resistance against adversarial clients, such that any coalition of malicious clients (within the tolerated threshold) can affect the aggregation result only by…
Oblivious transfer is a primitive of paramount importance in cryptography or, more precisely, two- and multi-party computation due to its universality. Unfortunately, oblivious transfer cannot be achieved in an unconditionally secure way…
Much research has been conducted to securely outsource multiple parties' data aggregation to an untrusted aggregator without disclosing each individual's data, or to enable multiple parties to jointly aggregate their data while preserving…
Secure aggregation enables a group of mutually distrustful parties, each holding private inputs, to collaboratively compute an aggregate value while preserving the privacy of their individual inputs. However, a major challenge in adopting…
In cloud databases, cloud computation over sensitive data uploaded by clients inevitably causes concern about data security and privacy. Even when encryption primitives and trusted computing environments are integrated into query processing…
This paper considers the secure aggregation problem for federated learning under an information theoretic cryptographic formulation, where distributed training nodes (referred to as users) train models based on their own local data and a…
We propose a new concept, oblivious quantum computation, which requires performing oblivious transfer with respect to the computation outcome of the quantum computation, where the secrecy of the input qubits and the program to identify the…