• Multi-Freq-LDPy: Multiple Freq...

Treffer: Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python

Title:
Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python
Authors:
Arcolezi, Héber Hwang, Gambs, Sébastien, Couchot, Jean-François, Palamidessi, Catuscia, Zolfaghari, Majid
Contributors:
Concurrency, Mobility and Transactions (COMETE), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X), Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de l'Institut Polytechnique de Paris, Centre Inria de Saclay, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre Inria de Saclay, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratory for Research on Technology for ECommerce (LATECE Laboratory - UQAM Montreal), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), ANR-17-EURE-0002,EIPHI,Ingénierie et Innovation par les sciences physiques, les savoir-faire technologiques et l'interdisciplinarité(2017), European Project: 835294,ERC-2018-ADG,ERC-2018-ADG,HYPATIA(2019)
Source:
ESORICS 2022 - European Symposium on Research in Computer Security. :770-775
Publisher Information:
CCSD; Springer Nature Switzerland, 2022.
Publication Year:
2022
Collection:
collection:X
collection:CNRS
collection:INRIA
collection:UNIV-FCOMTE
collection:UNIV-BM
collection:ENSMM
collection:FEMTO-ST
collection:LIX
collection:LIX-COMETE
collection:INRIA-SACLAY
collection:X-DEP-INFO
collection:INRIA_TEST
collection:UNIV-BM-THESE
collection:TESTALAIN1
collection:INRIA2
collection:IP_PARIS
collection:ANR
collection:GS-COMPUTER-SCIENCE
collection:INRIA-CANADA
collection:DEPARTEMENT-DE-MATHEMATIQUES
collection:IP-PARIS-INFORMATIQUE-DONNEES-ET-IA
Subject Terms:
Local Differential Privacy, Frequency Estimation, Multidimensional Data, Longitudinal Data, Open Source, [INFO]Computer Science [cs], [INFO.INFO-CR]Computer Science [cs], Cryptography and Security [cs.CR], [INFO.INFO-DB]Computer Science [cs], Databases [cs.DB], [INFO.INFO-DC]Computer Science [cs], Distributed, Parallel, and Cluster Computing [cs.DC]
Subject Geographic:
Copenhague, Denmark
Original Identifier:
ARXIV: 2205.02648
HAL: hal-03816212
Document Type:
Konferenz conferenceObject<br />Conference papers
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/arxiv/2205.02648; info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-17143-7_40; info:eu-repo/grantAgreement//835294/EU/Privacy and Utility Allied/HYPATIA
DOI:
10.1007/978-3-031-17143-7_40
Access URL:
https://inria.hal.science/hal-03816212
https://inria.hal.science/hal-03816212v1/document
https://inria.hal.science/hal-03816212v1/file/2022_Python_Multi_Freq_LDP.pdf
Rights:
info:eu-repo/semantics/OpenAccess
URL: http://creativecommons.org/licenses/by/
Accession Number:
edshal.hal.03816212v1
Database:
HAL

Weitere Informationen

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 four 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/).