TY - GEN
T1 - Multi-Freq-LDPy
T2 - 27th European Symposium on Research in Computer Security, ESORICS 2022
AU - Arcolezi, Héber H.
AU - Couchot, Jean François
AU - Gambs, Sébastien
AU - Palamidessi, Catuscia
AU - Zolfaghari, Majid
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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/ ).
AB - 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/ ).
KW - Frequency estimation
KW - Local differential privacy
KW - Longitudinal data
KW - Multidimensional data
KW - Open source
U2 - 10.1007/978-3-031-17143-7_40
DO - 10.1007/978-3-031-17143-7_40
M3 - Conference contribution
AN - SCOPUS:85140787962
SN - 9783031171420
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 770
EP - 775
BT - Computer Security – ESORICS 2022 - 27th European Symposium on Research in Computer Security, Proceedings
A2 - Atluri, Vijayalakshmi
A2 - Di Pietro, Roberto
A2 - Jensen, Christian D.
A2 - Meng, Weizhi
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 September 2022 through 30 September 2022
ER -