Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
A model of authorization for next-generation database systems
ACM Transactions on Database Systems (TODS)
Toward a multilevel secure relational data model
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
The multilevel relational (MLR) data model
ACM Transactions on Information and System Security (TISSEC)
User Interaction Design for Secure Systems
ICICS '02 Proceedings of the 4th International Conference on Information and Communications Security
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Interactive deduplication using active learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy Promises, Access Control, and Privacy Management
ISEC '02 Proceedings of the Third International Symposium on Electronic Commerce
Purpose based access control of complex data for privacy protection
Proceedings of the tenth ACM symposium on Access control models and technologies
Reference reconciliation in complex information spaces
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Limiting disclosure in hippocratic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
SP'88 Proceedings of the 1988 IEEE conference on Security and privacy
Editorial: Some issues in privacy data management
Data & Knowledge Engineering
Exclusive Strategy for Generalization Algorithms in Micro-data Disclosure
Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security
A composite privacy protection model
IWSEC'07 Proceedings of the Security 2nd international conference on Advances in information and computer security
k-jump strategy for preserving privacy in micro-data disclosure
Proceedings of the 13th International Conference on Database Theory
Systematic clustering method for l-diversity model
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Privacy-aware access control with generalization boundaries
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
Efficient inference control for open relational queries
DBSec'10 Proceedings of the 24th annual IFIP WG 11.3 working conference on Data and applications security and privacy
A privacy policy conflict detection method for multi-owner privacy data protection
Electronic Commerce Research
Privacy preservation and protection by extending generalized partial indices
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Towards an anti-inference (k, ℓ)-anonymity model with value association rules
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
| Hi-index | 0.00 |
The large availability of repositories storing various types of information about individuals has raised serious privacy concerns over the past decade. Nonetheless, database technology is far from providing adequate solutions to this problem that requires a delicate balance between an individual's privacy and convenience and data usability by enterprises and organizations - a database which is rigid and over-protective may render data of little value. Though these goals may seem odd, we argue that the development of solutions able to reconcile them will be an important challenge to be addressed in the next few years. We believe that the next wave of database technology will be represented by a DBMS that provides high-assurance privacy and security. In this paper, we elaborate on such challenges. In particular, we argue that we need to provide different views of data at a very fine level of granularity; conventional view technology is able to select only up to a single attribute value for a single tuple. We need to go even beyond this level. That is, we need a mechanism by which even a single value inside a tuple's attribute may have different views; we refer them as micro-views. We believe that such a mechanism can be an important building block, together with other mechanisms and tools, of the next wave of database technology.