Secure generalized deduplication via multi-key revealing encryption
Lucani, Daniel E. ; Nielsen, Lars ; Orlandi, Claudio ; Pagnin, Elena LU
and Vestergaard, Rasmus
(2020)
12th International Conference on Security and Cryptography for Networks, SCN 2020
In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12238 LNCS.
p.298-318
- Abstract
Cloud Storage Providers (CSPs) offer solutions to relieve users from locally storing vast amounts of data, including personal and sensitive ones. While users may desire to retain some privacy on the data they outsource, CSPs are interested in reducing the total storage space by employing compression techniques such as deduplication. We propose a new cryptographic primitive that simultaneously realizes both requirements: Multi-Key Revealing Encryption (MKRE). The goal of MKRE is to disclose the result of a pre-defined function over multiple ciphertexts, even if the ciphertexts were generated using different keys, while revealing nothing else about the data. We present a formal model and a security definition for MKRE and provide a... (More)
Cloud Storage Providers (CSPs) offer solutions to relieve users from locally storing vast amounts of data, including personal and sensitive ones. While users may desire to retain some privacy on the data they outsource, CSPs are interested in reducing the total storage space by employing compression techniques such as deduplication. We propose a new cryptographic primitive that simultaneously realizes both requirements: Multi-Key Revealing Encryption (MKRE). The goal of MKRE is to disclose the result of a pre-defined function over multiple ciphertexts, even if the ciphertexts were generated using different keys, while revealing nothing else about the data. We present a formal model and a security definition for MKRE and provide a construction of MKRE for generalized deduplication that only uses symmetric key primitives in a black-box way. Our construction allows (a) cloud providers to reduce the storage space by using generalized deduplication to compress encrypted data across users, and (b) each user to maintain a certain privacy level for the outsourced information. Our scheme can be proven secure in the random oracle model (and we argue that this is a necessary evil). We develop a proof-of-concept implementation of our solution. For a test data set, our MKRE construction achieves secure generalized deduplication with a compression ratio of 87% for 1 KB file chunks and 82.2% for 8 KB chunks. Finally, our experiments show that, compared to generalized deduplication setup with un-encrypted files, adding privacy via MKRE introduces a compression overhead of less than $$3\%$$ and reduces the storage throughput by at most $$6.9\%$$.
(Less)
- author
- Lucani, Daniel E.
; Nielsen, Lars
; Orlandi, Claudio
; Pagnin, Elena
LU
and Vestergaard, Rasmus
- organization
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Private cloud storage, Revealing encryption, Secure deduplication
- host publication
- Security and Cryptography for Networks - 12th International Conference, SCN 2020, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- editor
- Galdi, Clemente and Kolesnikov, Vladimir
- volume
- 12238 LNCS
- pages
- 21 pages
- publisher
- Springer Science and Business Media B.V.
- conference name
- 12th International Conference on Security and Cryptography for Networks, SCN 2020
- conference location
- Amalfi, Italy
- conference dates
- 2020-09-14 - 2020-09-16
- external identifiers
-
- scopus:85091163821
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 9783030579890
- DOI
- 10.1007/978-3-030-57990-6_15
- project
- Säkra mjukvaruuppdateringar för den smarta staden
- language
- English
- LU publication?
- yes
- id
- 1ed76230-e38a-4871-b000-2f1a089a5d35
- date added to LUP
- 2020-10-28 10:24:13
- date last changed
- 2024-06-27 00:05:34
@inproceedings{1ed76230-e38a-4871-b000-2f1a089a5d35,
abstract = {{<p>Cloud Storage Providers (CSPs) offer solutions to relieve users from locally storing vast amounts of data, including personal and sensitive ones. While users may desire to retain some privacy on the data they outsource, CSPs are interested in reducing the total storage space by employing compression techniques such as deduplication. We propose a new cryptographic primitive that simultaneously realizes both requirements: Multi-Key Revealing Encryption (MKRE). The goal of MKRE is to disclose the result of a pre-defined function over multiple ciphertexts, even if the ciphertexts were generated using different keys, while revealing nothing else about the data. We present a formal model and a security definition for MKRE and provide a construction of MKRE for generalized deduplication that only uses symmetric key primitives in a black-box way. Our construction allows (a) cloud providers to reduce the storage space by using generalized deduplication to compress encrypted data across users, and (b) each user to maintain a certain privacy level for the outsourced information. Our scheme can be proven secure in the random oracle model (and we argue that this is a necessary evil). We develop a proof-of-concept implementation of our solution. For a test data set, our MKRE construction achieves secure generalized deduplication with a compression ratio of 87% for 1 KB file chunks and 82.2% for 8 KB chunks. Finally, our experiments show that, compared to generalized deduplication setup with un-encrypted files, adding privacy via MKRE introduces a compression overhead of less than $$3\%$$ and reduces the storage throughput by at most $$6.9\%$$.</p>}},
author = {{Lucani, Daniel E. and Nielsen, Lars and Orlandi, Claudio and Pagnin, Elena and Vestergaard, Rasmus}},
booktitle = {{Security and Cryptography for Networks - 12th International Conference, SCN 2020, Proceedings}},
editor = {{Galdi, Clemente and Kolesnikov, Vladimir}},
isbn = {{9783030579890}},
issn = {{1611-3349}},
keywords = {{Private cloud storage; Revealing encryption; Secure deduplication}},
language = {{eng}},
pages = {{298--318}},
publisher = {{Springer Science and Business Media B.V.}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{Secure generalized deduplication via multi-key revealing encryption}},
url = {{http://dx.doi.org/10.1007/978-3-030-57990-6_15}},
doi = {{10.1007/978-3-030-57990-6_15}},
volume = {{12238 LNCS}},
year = {{2020}},
}