Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Sparse Federated Analytics: Location Heatmaps under Distributed
  Differential Privacy with Secure Aggregation   We design a scalable algorithm to privately generate location heatmaps over
decentralized data from millions of user devices. It aims to ensure
differential privacy before data becomes visible to a service provider while
maintaining high data accuracy and minimizing resource consumption on users'
devices. To achieve this, we revisit the distributed differential privacy
concept based on recent results in the secure multiparty computation field and
design a scalable and adaptive distributed differential privacy approach for
location analytics. Evaluation on public location datasets shows that this
approach successfully generates metropolitan-scale heatmaps from millions of
user samples with a worst-case client communication overhead that is
significantly smaller than existing state-of-the-art private protocols of
similar accuracy.

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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: comp-gas