Yggdrasil: Privacy-Aware Dual Deduplication in Multi Client Settings
Sehat, Hadi ; Pagnin, Elena LU
and Lucani, Daniel E.
(2021)
IEEE International Conference on Communications (ICC) 2021
p.1-1
- Abstract
- This paper proposes Yggdrasil, a protocol for privacy-aware dual data deduplication in multi-client settings. Yggdrasil is designed to reduce cloud storage space while safeguarding the privacy of clients’ data. This is achieved by exploiting a ‘dual’ setting, where both the cloud and the clients store a fraction of the data. Yggdrasil combines two innovative techniques to achieve this goal. First, generalized deduplication, an emerging solution to reduce data footprint; second, non- deterministic lightweight transformations that ensure a high level of privacy while improving the degree of cross-user data compression in the cloud. Our client preprocessing guarantees that an honest-but-curious cloud storage provider faces a high degree of... (More)
- This paper proposes Yggdrasil, a protocol for privacy-aware dual data deduplication in multi-client settings. Yggdrasil is designed to reduce cloud storage space while safeguarding the privacy of clients’ data. This is achieved by exploiting a ‘dual’ setting, where both the cloud and the clients store a fraction of the data. Yggdrasil combines two innovative techniques to achieve this goal. First, generalized deduplication, an emerging solution to reduce data footprint; second, non- deterministic lightweight transformations that ensure a high level of privacy while improving the degree of cross-user data compression in the cloud. Our client preprocessing guarantees that an honest-but-curious cloud storage provider faces a high degree of uncertainty in determining the original clients’ data. We introduce an uncertainty metric to measure the privacy of the client’s outsourced data and three compression metrics to investigate the performance of Yggdrasil. Our experiments with a dataset of DVI files show that Yggdrasil achieves an overall compression rate of 43%, which means that Yggdrasil can represent the same database using less than half of the original space. Moreover, for the same experiment clients only store 17% of the original data, the cloud hosts the remaining 26%, and the client preprocessing ensures each outsourced fragment has 10 293 possible original strings. Higher uncertainty is possible, but reduces the cloud’s compression capability. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a3d1bdb9-88aa-49d2-bb79-59deea227516
- author
- Sehat, Hadi
; Pagnin, Elena
LU
and Lucani, Daniel E.
- organization
- publishing date
- 2021-06-14
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- ICC 2021 - IEEE International Conference on Communications
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE International Conference on Communications (ICC) 2021
- conference location
- Montreal, Canada
- conference dates
- 2021-06-14 - 2021-06-23
- external identifiers
-
- scopus:85115693531
- ISBN
- 978-1-7281-7123-4
- 978-1-7281-7122-7
- DOI
- 10.1109/ICC42927.2021.9500816
- project
- Säkra mjukvaruuppdateringar för den smarta staden
- language
- English
- LU publication?
- yes
- id
- a3d1bdb9-88aa-49d2-bb79-59deea227516
- date added to LUP
- 2021-09-06 11:16:10
- date last changed
- 2024-06-16 18:22:02
@inproceedings{a3d1bdb9-88aa-49d2-bb79-59deea227516,
abstract = {{This paper proposes Yggdrasil, a protocol for privacy-aware dual data deduplication in multi-client settings. Yggdrasil is designed to reduce cloud storage space while safeguarding the privacy of clients’ data. This is achieved by exploiting a ‘dual’ setting, where both the cloud and the clients store a fraction of the data. Yggdrasil combines two innovative techniques to achieve this goal. First, generalized deduplication, an emerging solution to reduce data footprint; second, non- deterministic lightweight transformations that ensure a high level of privacy while improving the degree of cross-user data compression in the cloud. Our client preprocessing guarantees that an honest-but-curious cloud storage provider faces a high degree of uncertainty in determining the original clients’ data. We introduce an uncertainty metric to measure the privacy of the client’s outsourced data and three compression metrics to investigate the performance of Yggdrasil. Our experiments with a dataset of DVI files show that Yggdrasil achieves an overall compression rate of 43%, which means that Yggdrasil can represent the same database using less than half of the original space. Moreover, for the same experiment clients only store 17% of the original data, the cloud hosts the remaining 26%, and the client preprocessing ensures each outsourced fragment has 10 293 possible original strings. Higher uncertainty is possible, but reduces the cloud’s compression capability.}},
author = {{Sehat, Hadi and Pagnin, Elena and Lucani, Daniel E.}},
booktitle = {{ICC 2021 - IEEE International Conference on Communications}},
isbn = {{978-1-7281-7123-4}},
language = {{eng}},
month = {{06}},
pages = {{1--1}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Yggdrasil: Privacy-Aware Dual Deduplication in Multi Client Settings}},
url = {{http://dx.doi.org/10.1109/ICC42927.2021.9500816}},
doi = {{10.1109/ICC42927.2021.9500816}},
year = {{2021}},
}