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Practical Attacks o...
Practical Attacks on Relational Databases Protected via Searchable Encryption
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- Abdelraheem, Mohamed (author)
- Intelligent Voice Ltd, UK
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- Andersson, Tobias (author)
- RISE,SICS,RISE SICS AB
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- Gehrmann, Christian (author)
- Lund University,Lunds universitet,Nätverk och säkerhet,Forskargrupper vid Lunds universitet,Networks and Security,Lund University Research Groups
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- Glackin, Cornelius (author)
- Intelligent Voice Ltd, UK
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Chen, Liqun (editor)
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Manulis, Mark (editor)
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Schneider, Steve (editor)
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(creator_code:org_t)
- 2018-08-15
- 2018
- English.
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In: Lecture Notes in Computer Science. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783319991351 ; , s. 171-191
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http://dx.doi.org/10...
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https://urn.kb.se/re...
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Abstract
Subject headings
Close
- Searchable symmetric encryption (SSE) schemes are commonly proposed to enable search in a protected unstructured documents such as email archives or any set of sensitive text files. However, some SSE schemes have been recently proposed in order to protect relational databases. Most of the previous attacks on SSE schemes have only targeted its common use case, protecting unstructured data. In this work, we propose a new inference attack on relational databases protected via SSE schemes. Our inference attack enables a passive adversary with only basic knowledge about the meta-data information of the target relational database to recover the attribute names of some observed queries. This violates query privacy since the attribute name of a query is secret.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
Keyword
- Query languages
- Query processing
- Security of data
- E-mail archives
- Inference attacks
- Passive adversary
- Relational Database
- Searchable encryptions
- Symmetric encryption
- Unstructured data
- Unstructured documents
- Cryptography
Publication and Content Type
- ref (subject category)
- kon (subject category)
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