An empirical study of natural language parsing of privacy policy rules using the SPARCLE policy workbench

Today organizations do not have good ways of linking their written privacy policies with the implementation of those policies. To assist organizations in addressing this issue, our human-centered research has focused on understanding organizational privacy management needs, and, based on those needs, creating a usable and effective policy workbench called SPARCLE. SPARCLE will enable organizational users to enter policies in natural language, parse the policies to identify policy elements and then generate a machine readable (XML) version of the policy. In the future, SPARCLE will then enable mapping of policies to the organization's configuration and provide audit and compliance tools to ensure that the policy implementation operates as intended. In this paper, we present the strategies employed in the design and implementation of the natural language parsing capabilities that are part of the functional version of the SPARCLE authoring utility. We have created a set of grammars which execute on a shallow parser that are designed to identify the rule elements in privacy policy rules. We present empirical usability evaluation data from target organizational users of the SPARCLE system and highlight the parsing accuracy of the system with the organizations' privacy policies. The successful implementation of the parsing capabilities is an important step towards our goal of providing a usable and effective method for organizations to link the natural language version of privacy policies to their implementation, and subsequent verification through compliance auditing of the enforcement logs.