TY - GEN
T1 - Query-independent dynamic similitude data models for edge-computing
AU - Derbeko, Philip
AU - Dolev, Shlomi
AU - Gudes, Ehud
N1 - Funding Information:
We thank the Lynne and William Frankel Center for Computer Science, the Rita Altura Trust Chair in Computer Science. This research was also supported by a grant from the Ministry of Science Technology, Israel the Japan Science and Technology Agency (JST), Japan, and DFG German-Israeli collaborative projects.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - Big Data
KW - Data Reduction
KW - Differential Privacy
KW - Privacy
KW - Similitude Model
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=85087628609&partnerID=8YFLogxK
U2 - 10.1109/INFOCOMWKSHPS47286.2019.9093754
DO - 10.1109/INFOCOMWKSHPS47286.2019.9093754
M3 - Conference contribution
AN - SCOPUS:85087628609
T3 - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
BT - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 INFOCOM IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
Y2 - 29 April 2019 through 2 May 2019
ER -