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Achieving privacy preservation in a data-sharing computing environment is becoming a challenging problem. Some organisations may have published privacy policies, which promise privacy protection practices on data collection, use and disclosure, but these practices may not be implemented. To maintain consistency between the privacy policy and the practices, privacy protection requirements in privacy policy should be formally specified. In specifying privacy policy, we use purpose as the basis of access control. In this paper, we extend our previous work to specify purpose management. Purpose can be divided into two categories: intended purpose and access purpose. Privacy policy is to ensure that data can only be used for its intended purpose, and the access purpose should be compliant with the data's intended purpose. We specify entities in the purpose-based access control model. Using the technique of VDM, we then specify the invariants corresponding to the privacy requirements in privacy policy, and then specify the operations in the model and investigate their proof obligations.

Summary Objectives: This paper focusses on the person identification problem based on features extracted from the ElectroEncephaloGram (EEG). A bilinear rather than a purely linear model is fitted on the EEG signal, prompted by the existence of non-linear components in the EEG signal – a conjecture already investigated in previous research works. The novelty of the present work lies in the comparison between the linear and the bilinear results, obtained from real field EEG data, aiming towards identification of healthy subjects rather than classification of pathological cases for diagnosis. Methods: The EEG signal of a, in principle, healthy individual is processed via (non)linear (AR, bilinear) methods and classified by an artificial neural network classifier. Results: Experiments performed on real field data show that utilization of the bilinear model parameters as features improves correct classification scores at the cost of increased complexity and computations. Results are seen to be statistically significant at the 99.5% level of significance, via the χ2 test for contingency. Conclusions: The results obtained in the present study further corroborate existing research, which shows evidence that the EEG carries individual-specific information, and that it can be successfully exploited for purposes of person identification and authentication.

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