Lying about Private Information: An Ethical Justification

Lying motivated by a fear of misusing private information is a key issue in ethics with many important applications in law, business, politics, and psychology. In this paper, lying is separated according to two types of assertions: lying about non-private information and lying about private information. This distinction is applied to the case of the \u27known murderer\u27 in the context of Kant\u27s categorical imperative. The main result of the paper is narrowing the ethical dilemma to a choice between \u27a zero private information society\u27 and \u27an alTlying-about-private-information society\u27. We claim that privacy as a universal requirement supports lying about private information in order to avoid harm. This thesis has been applied to a current situation where customers, who are lying about their private information, are met with moral outrage and loss of credibility. Our conclusion is that enterprises have to modify their strategies and consider lying about private information, mainly, as a descriptive non-moral phenomenon that can be handled through primarily non-normative measures

An Approach for Managing Access to Personal Information Using Ontology-Based Chains

The importance of electronic healthcare has caused numerous changes in both substantive and procedural aspects of healthcare processes. These changes have produced new challenges to patient privacy and information secrecy. Traditional privacy policies cannot respond to rapidly increased privacy needs of patients in electronic healthcare. Technically enforceable privacy policies are needed in order to protect patient privacy in modern healthcare with its cross organisational information sharing and decision making. This thesis proposes a personal information flow model that specifies a limited number of acts on this type of information. Ontology classified Chains of these acts can be used instead of the "intended/business purposes" used in privacy access control to seamlessly imbuing current healthcare applications and their supporting infrastructure with security and privacy functionality. In this thesis, we first introduce an integrated basic architecture, design principles, and implementation techniques for privacy-preserving data mining systems. We then discuss the key methods of privacypreserving data mining systems which include four main methods: Role based access control (RBAC), Hippocratic database, Chain method and eXtensible Access Control Markup Language (XACML). We found out that the traditional methods suffer from two main problems: complexity of privacy policy design and the lack of context flexibility that is needed while working in critical situations such as the one we find in hospitals. We present and compare strategies for realising these methods. Theoretical analysis and experimental evaluation show that our new method can generate accurate data mining models and safe data access management while protecting the privacy of the data being mined. The experiments followed comparative kind of experiments, to show the ease of the design first and then follow real scenarios to show the context flexibility in saving personal information privacy of our investigated method