79,597 research outputs found
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
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 number of users is the overhead of secure model aggregation across many
users. In particular, the overhead of the state-of-the-art protocols for secure
model aggregation grows quadratically with the number of users. In this paper,
we propose the first secure aggregation framework, named Turbo-Aggregate, that
in a network with users achieves a secure aggregation overhead of
, as opposed to , while tolerating up to a user dropout
rate of . Turbo-Aggregate employs a multi-group circular strategy for
efficient model aggregation, and leverages additive secret sharing and novel
coding techniques for injecting aggregation redundancy in order to handle user
dropouts while guaranteeing user privacy. We experimentally demonstrate that
Turbo-Aggregate achieves a total running time that grows almost linear in the
number of users, and provides up to speedup over the
state-of-the-art protocols with up to users. Our experiments also
demonstrate the impact of model size and bandwidth on the performance of
Turbo-Aggregate
Fuzzy-based Augmentation of Federated Averaging for Enhanced Decentralized Machine Learning
Federated Averaging (FedAvg) is a leading decentralized machine learning approach, prioritizing data privacy. However, it faces challenges like non-identically distributed data, communication bottlenecks, and adversarial attacks. This abstract introduces a fuzzy-based FedAvg, leveraging fuzzy logic to manage uncertainty in decentralized environments. Fuzzy clustering adapts the model to varied data distributions, addressing non-IID challenges. Fuzzy membership functions enhance aggregation by introducing an adaptive weighting scheme, improving convergence and accuracy. The fuzzy approach incorporates privacy-preserving mechanisms, ensuring secure aggregation with homomorphic encryption and differential privacy. Simulations show improved convergence, resilience to non-IID data, and enhanced privacy compared to traditional FedAvg, contributing to more secure decentralized ML systems
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
We consider training models on private data that is distributed across user
devices. To ensure privacy, we add on-device noise and use secure aggregation
so that only the noisy sum is revealed to the server. We present a
comprehensive end-to-end system, which appropriately discretizes the data and
adds discrete Gaussian noise before performing secure aggregation. We provide a
novel privacy analysis for sums of discrete Gaussians. We also analyze the
effect of rounding the input data and the modular summation arithmetic. Our
theoretical guarantees highlight the complex tension between communication,
privacy, and accuracy. Our extensive experimental results demonstrate that our
solution is essentially able to achieve a comparable accuracy to central
differential privacy with 16 bits of precision per value
SDAMQ: Secure Data Aggregation for Multiple Queries in Wireless Sensor Networks
Wireless Sensor Network consists of severely energy constrained sensor nodes and are susceptible to security attacks due to broadcast communication model. It is necessary to optimize the transmission of packets to reduce the energy consumption. In addition data has to be encrypted in order to overcome the attack from the compromising nodes. We propose Secure Data Aggregation for Multiple Queries (SDAMQ) in Wireless Sensor Networks where multiple aggregate queries from the sink are authenticated and distributed to the sensor nodes. The sensor nodes respond by aggregating data belonging to multiple coexisting queries into a single packet, there by reducing the transmission cost. The intermediary nodes aggregate the encrypted data using additively homomorphic encryption. Thus authenticated query propagation combined with homomorphic encryption provide secure data aggregation at low energy consumption. Simulation results shows that SDAMQ provides better performance
A Toolchain for Privacy-Preserving Distributed Aggregation on Edge-Devices
Valuable insights, such as frequently visited environments in the wake of the
COVID-19 pandemic, can oftentimes only be gained by analyzing sensitive data
spread across edge-devices like smartphones. To facilitate such an analysis, we
present a toolchain for a distributed, privacy-preserving aggregation of local
data by taking the limited resources of edge-devices into account. The
distributed aggregation is based on secure summation and simultaneously
satisfies the notion of differential privacy. In this way, other parties can
neither learn the sensitive data of single clients nor a single client's
influence on the final result. We perform an evaluation of the power
consumption, the running time and the bandwidth overhead on real as well as
simulated devices and demonstrate the flexibility of our toolchain by
presenting an extension of the summation of histograms to distributed
clustering
Privacy-enhancing distributed protocol for data aggregation based on blockchain and homomorphic encryption
The recent increase in reported incidents of security breaches compromising users' privacy call into question the current centralized model in which third-parties collect and control massive amounts of personal data. Blockchain has demonstrated that trusted and auditable computing is possible using a decentralized network of peers accompanied by a public ledger. Furthermore, Homomorphic Encryption (HE) guarantees confidentiality not only on the computation but also on the transmission, and storage processes. The synergy between Blockchain and HE is rapidly increasing in the computing environment.
This research proposes a privacy-enhancing distributed and secure protocol for data aggregation backboned by Blockchain and HE technologies. Blockchain acts as a distributed ledger which facilitates efficient data aggregation through a Smart Contract. On the top, HE will be used for data encryption allowing private aggregation operations. The theoretical description, potential applications, a suggested implementation and a performance analysis are presented to validate the proposed solution.This work has been partially supported by the Basque Country Government under the ELKARTEK program, project TRUSTIND (KK- 2020/00054). It has also been partially supported by the H2020 TERMINET project (GA 957406)
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