@inproceedings{026fb0d1532b40e3b4373334e73c0828,
title = "Query-independent dynamic similitude data models for edge-computing",
abstract = "Privacy-preserving data release is an increasingly important problem in today's ubiquitous computing. Performing data processing on edge devices saves CPU time, network traffic and improves latency. The savings are especially significant for edge devices with low-power and limited resources. In addition, processing and privacy-preserving release of the data on the edge devices prevents sensitive data from leaving the devices at all. Thus, solving a bunch of potential problems in data protection. We propose a usage of query-independent, similitude models for privacy-preserving data release on the edge devices. Those models build a compact representation of the data for any possible query, as opposite to a common method of queryspecific data release. Compared to the query-specific models, similitude models can be combined and subtracted when a new edge devices appear or shutdown. In addition, a data oriented approach allows further processing in a fog, near the edge devices.",
keywords = "Big Data, Data Reduction, Differential Privacy, Privacy, Similitude Model, Wavelets",
author = "Philip Derbeko and Shlomi Dolev and Ehud Gudes",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 INFOCOM IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019 ; Conference date: 29-04-2019 Through 02-05-2019",
year = "2019",
month = apr,
day = "1",
doi = "10.1109/INFOCOMWKSHPS47286.2019.9093754",
language = "English",
series = "INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019",
address = "United States",
}