Abstract
This paper introduces the multi-freq-ldpy Python package for multiple frequency estimation under Local Differential Privacy (LDP) guarantees. LDP is a gold standard for achieving local privacy with several real-world implementations by big tech companies such as Google, Apple, and Microsoft. The primary application of LDP is frequency (or histogram) estimation, in which the aggregator estimates the number of times each value has been reported. The presented package provides an easy-to-use and fast implementation of state-of-the-art solutions and LDP protocols for frequency estimation of: single attribute (i.e., the building blocks), multiple attributes (i.e., multidimensional data), multiple collections (i.e., longitudinal data), and both multiple attributes/collections. Multi-freq-ldpy is built on the well-established Numpy package – a de facto standard for scientific computing in Python – and the Numba package for fast execution. These features are described and illustrated in this paper with two worked examples. This package is open-source and publicly available under an MIT license via GitHub (https://github.com/hharcolezi/multi-freq-ldpy) and can be installed via PyPi (https://pypi.org/project/multi-freq-ldpy/).
Authors are listed by order of contribution. See [3] for the full version of this paper
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Notes
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- 2.
A more complete Python package for single frequency estimation can be found in (https://pypi.org/project/pure-ldp/) [5].
- 3.
Originally known as basic one-time RAPPOR [9].
- 4.
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Acknowledgements
This work was supported by the European Research Council (ERC) project HYPATIA under the European Union’s Horizon 2020 research and innovation programme. Grant agreement n. 835294. The work of Jean-François Couchot was supported by the EIPHI-BFC Graduate School (contract “ANR-17-EURE-0002"). Sébastien Gambs is supported by the Canada Research Chair program as well as a Discovery Grant from NSERC.
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Arcolezi, H.H., Couchot, JF., Gambs, S., Palamidessi, C., Zolfaghari, M. (2022). Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python. In: Atluri, V., Di Pietro, R., Jensen, C.D., Meng, W. (eds) Computer Security – ESORICS 2022. ESORICS 2022. Lecture Notes in Computer Science, vol 13556. Springer, Cham. https://doi.org/10.1007/978-3-031-17143-7_40
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