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Modeling and assessing inference exposure in encrypted databases
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Source ACM Transactions on Information and System Security (TISSEC) archive
Volume 8 ,  Issue 1  (February 2005) table of contents
Pages: 119 - 152  
Year of Publication: 2005
ISSN:1094-9224
Authors
Alberto Ceselli  Università di Milano, Crema, Italy
Ernesto Damiani  Università di Milano, Crema, Italy
Sabrina De Capitani Di Vimercati  Università di Milano, Crema, Italy
Sushil Jajodia  George Mason University, Fairfax, VA
Stefano Paraboschi  Università di Bergamo, Dalmine---Italy
Pierangela Samarati  Università di Milano, Crema, Italy
Publisher
ACM  New York, NY, USA
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ABSTRACT

The scope and character of today's computing environments are progressively shifting from traditional, one-on-one client-server interaction to the new cooperative paradigm. It then becomes of primary importance to provide means of protecting the secrecy of the information, while guaranteeing its availability to legitimate clients. Operating online querying services securely on open networks is very difficult; therefore many enterprises outsource their data center operations to external application service providers. A promising direction toward prevention of unauthorized access to outsourced data is represented by encryption. However, data encryption is often supported for the sole purpose of protecting the data in storage while allowing access to plaintext values by the server, which decrypts data for query execution. In this paper, we present a simple yet robust single-server solution for remote querying of encrypted databases on external servers. Our approach is based on the use of indexing information attached to the encrypted database, which can be used by the server to select the data to be returned in response to a query without the need of accessing the plaintext database content. Our indexes balance the trade-off between efficiency requirements in query execution and protection requirements due to possible inference attacks exploiting indexing information. We investigate quantitative measures to model inference exposure and provide some related experimental results.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY  6

Collaborative Colleagues:
Alberto Ceselli: colleagues
Ernesto Damiani: colleagues
Sabrina De Capitani Di Vimercati: colleagues
Sushil Jajodia: colleagues
Stefano Paraboschi: colleagues
Pierangela Samarati: colleagues