Vertical partitioning algorithms for database design
ACM Transactions on Database Systems (TODS)
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
An Effective Approach to Vertical Partitioning for Physical Design of Relational Databases
IEEE Transactions on Software Engineering
Executing SQL over encrypted data in the database-service-provider model
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Weaving Relations for Cache Performance
Proceedings of the 27th International Conference on Very Large Data Bases
Balancing confidentiality and efficiency in untrusted relational DBMSs
Proceedings of the 10th ACM conference on Computer and communications security
Integrating vertical and horizontal partitioning into automated physical database design
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Order preserving encryption for numeric data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Key management for multi-user encrypted databases
Proceedings of the 2005 ACM workshop on Storage security and survivability
A privacy-preserving index for range queries
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Design and analysis of querying encrypted data in relational databases
Proceedings of the 21st annual IFIP WG 11.3 working conference on Data and applications security
Building disclosure risk aware query optimizers for relational databases
Proceedings of the VLDB Endowment
Adaptive, secure, and scalable distributed data outsourcing: a vision paper
Proceedings of the 2011 workshop on Dynamic distributed data-intensive applications, programming abstractions, and systems
Proceedings of the First International Conference on Security of Internet of Things
| Hi-index | 0.00 |
Security and privacy concerns, as well as legal considerations, force many companies to encrypt the sensitive data in their databases. However, storing the data in encrypted format entails significant performance penalties during query processing. In this paper, we address several design issues related to querying encrypted relational databases. The experiments we conducted on benchmark datasets show that excessive decryption costs during query processing result in CPU bottleneck. As a solution we propose a new method based on schema decomposition that partitions sensitive and non-sensitive attributes of a relation into two separate relations. Our method improves the system performance dramatically by parallelizing disk IO latency with CPU-intensive operations (i.e., encryption/decryption).