Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Philip-William Grassal"'
Publikováno v:
Energy Informatics, Vol 1, Iss S1, Pp 93-113 (2018)
Abstract There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly
Externí odkaz:
https://doaj.org/article/a6e528b132664232858c6086e210c78f
Publikováno v:
Data and Applications Security and Privacy XXXV ISBN: 9783030812416
DBSec
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Data and Applications Security and Privacy XXXV
35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec 2021)
DBSec
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Data and Applications Security and Privacy XXXV
35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec 2021)
Attacks that aim to identify the training data of neural networks represent a severe threat to the privacy of individuals in the training dataset. A possible protection is offered by anonymization of the training data or training function with differ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::475db1987ed599352826a35ff75e7a48
https://doi.org/10.1007/978-3-030-81242-3_2
https://doi.org/10.1007/978-3-030-81242-3_2
Publikováno v:
Energy Informatics, S1 (48), 93-113
Energy Informatics, Vol 1, Iss S1, Pp 93-113 (2018)
Energy Informatics, Vol 1, Iss S1, Pp 93-113 (2018)
There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly limited
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f23d259cd72dfff36ba7281dad2d45c
Publikováno v:
Proceedings of the VLDB Endowment. 14(13):3335-3347
Differential privacy allows bounding the influence that training data records have on a machine learning model. To use differential privacy in machine learning, data scientists must choose privacy parameters (ϵ, δ ). Choosing meaningful privacy par
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cac8c1bff56ec2447cd43b4075eca731