7,182 research outputs found
Dynamically Weighted Federated k-Means
Federated clustering, an integral aspect of federated machine learning,
enables multiple data sources to collaboratively cluster their data,
maintaining decentralization and preserving privacy. In this paper, we
introduce a novel federated clustering algorithm named Dynamically Weighted
Federated k-means (DWF k-means) based on Lloyd's method for k-means clustering,
to address the challenges associated with distributed data sources and
heterogeneous data. Our proposed algorithm combines the benefits of traditional
clustering techniques with the privacy and scalability benefits offered by
federated learning. The algorithm facilitates collaborative clustering among
multiple data owners, allowing them to cluster their local data collectively
while exchanging minimal information with the central coordinator. The
algorithm optimizes the clustering process by adaptively aggregating cluster
assignments and centroids from each data source, thereby learning a global
clustering solution that reflects the collective knowledge of the entire
federated network. We address the issue of empty clusters, which commonly
arises in the context of federated clustering. We conduct experiments on
multiple datasets and data distribution settings to evaluate the performance of
our algorithm in terms of clustering score, accuracy, and v-measure. The
results demonstrate that our approach can match the performance of the
centralized classical k-means baseline, and outperform existing federated
clustering methods like k-FED in realistic scenarios
CryptGraph: Privacy Preserving Graph Analytics on Encrypted Graph
Many graph mining and analysis services have been deployed on the cloud,
which can alleviate users from the burden of implementing and maintaining graph
algorithms. However, putting graph analytics on the cloud can invade users'
privacy. To solve this problem, we propose CryptGraph, which runs graph
analytics on encrypted graph to preserve the privacy of both users' graph data
and the analytic results. In CryptGraph, users encrypt their graphs before
uploading them to the cloud. The cloud runs graph analysis on the encrypted
graphs and obtains results which are also in encrypted form that the cloud
cannot decipher. During the process of computing, the encrypted graphs are
never decrypted on the cloud side. The encrypted results are sent back to users
and users perform the decryption to obtain the plaintext results. In this
process, users' graphs and the analytics results are both encrypted and the
cloud knows neither of them. Thereby, users' privacy can be strongly protected.
Meanwhile, with the help of homomorphic encryption, the results analyzed from
the encrypted graphs are guaranteed to be correct. In this paper, we present
how to encrypt a graph using homomorphic encryption and how to query the
structure of an encrypted graph by computing polynomials. To solve the problem
that certain operations are not executable on encrypted graphs, we propose hard
computation outsourcing to seek help from users. Using two graph algorithms as
examples, we show how to apply our methods to perform analytics on encrypted
graphs. Experiments on two datasets demonstrate the correctness and feasibility
of our methods
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