Relative compromise of statistical databases
Australian Computer Journal
Computers, ethics & social values
Computers, ethics & social values
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the impact of knowledge discovery and data mining
CRPIT '00 Selected papers from the second Australian Institute conference on Computer ethics
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Heuristic Measures of Interestingness
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Randomization in privacy preserving data mining
ACM SIGKDD Explorations Newsletter
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Guiding knowledge discovery through interactive data mining
Managing data mining technologies in organizations
Preface: proceedings of the ICDM 2002 workshop on privacy, security, and data mining
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Privacy conflicts in CRM services for online shops: a case study
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Exploratory medical knowledge discovery: experiences and issues
ACM SIGKDD Explorations Newsletter
Experiences in building a tool for navigating association rule result sets
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
How sensitive is your personal information?
Proceedings of the 2007 ACM symposium on Applied computing
Controlling inference: avoiding p-level reduction during analysis
ACSW '07 Proceedings of the fifth Australasian symposium on ACSW frontiers - Volume 68
Privacy as a base for breaching confidentiality
AIC'05 Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications
Data reduction approach for sensitive associative classification rule hiding
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Data privacy protection in multi-party clustering
Data & Knowledge Engineering
A Heuristic Data Reduction Approach for Associative Classification Rule Hiding
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Surveillance, persuasion, and panopticon
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
Practical issues on privacy-preserving health data mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Developing artificial agents worthy of trust: "Would you buy a used car from this artificial agent?"
Ethics and Information Technology
Associative classification rules hiding for privacy preservation
International Journal of Intelligent Information and Database Systems
Privacy measures for free text documents: bridging the gap between theory and practice
TrustBus'11 Proceedings of the 8th international conference on Trust, privacy and security in digital business
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Knowledge discovery allows considerable insight into data. This brings with it the inherent risk that what is inferred may be private or ethically sensitive. The process of generating rules through a mining operation becomes an ethical issue when the results are used in decision making processes that effect people, or when mining customer data unwittingly compromises the privacy of those customers.Significantly, the sensitivity of a rule not be apparent to the miner, particularly since the volume and diversity of rules can often be large. However, given the subjective nature of such sensitivity, rather than prohibit the production of ethically and privacy sensitive rules, we present here an alerting process that detects and highlights the sensitivity of the discovered rules. The process caters for differing sensitivities at the attribute value level and allows a variety of sensitivity combination functions to be employed. These functions have been tested empirically and the results of these tests are reported.