Browsing by Subject "Privacy Policy"
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Item A DATA MINING APPROACH TO COMPARE PRIVACY POLICIES(2017-01-01) Alduaij, Shaikha; Chen, Zhiyuan; Information Systems; Information SystemsAs it becomes easier and less expensive for service providers to store huge amounts of data, the information collected about individuals is growing rapidly. At the same time, individuals' concerns about their privacy is increasing. Although most service providers use privacy policies to explain what information they are collecting, who will access it, and for what purpose, existing research shows that users often do not read privacy policies or they find privacy policies difficult to understand. Thus, users may not make the right regarding securing their privacy. Some studies have proposed tools to enhance the effectiveness of privacy policies and thus facilitate decision making, whereas others have introduced visualization models to increase privacy usability and effectiveness. Despite all of this, there has been relatively little work done on providing a comparison model to assist users when comparing the privacy practices of different companies in an effort to make informed decisions. In this study, we first analyze users' awareness of privacy policies and the privacy practices described in them, their privacy concerns, and their privacy needs. Next, we use text mining techniques to extract information users care about such as collected information, shared information, and provided controls. Unlike existing techniques, our approach attempts to avoid the use of patterns or rules as much as possible because the format of privacy policies often changes over time, and therefore, patterns and rules often become obsolete. We then develop a comparison tool to show the extracted information side by side. Then we conduct a survey to validate our comparison tool and gather users' privacy preferences. Because a side-by-side comparison may not work well when users are comparing a large number of policies, we propose a data mining based method to rank privacy policies. Unlike existing techniques that rely on user ratings, which are often not reliable, our approach relies on pair-wise preferences given by users, which are often a lot more reliable.Item DATA PRIVACY MANAGEMENT USING PRIVACY COMPLIANT BLOCKCHAIN STRUCTURES(2018-01-01) Banerjee, Agniva; Joshi, Karuna Pande; Computer Science and Electrical Engineering; Computer ScienceAn important requirement of any information management system is to protect data and resources against leak or improper modifications, while at the same time ensure data availability to legitimate users. Moreover, systems handling personal data must also track its provenance and be regularly audited to comply with regulations. By assuring auditable, privacy policy compliant actions, we can also guarantee that areas where privacy policies have been technically enforced are highlighted. As part of this theses, we have built a novel framework to automatically track details about how a consumer's private data is stored, used and shared by a Cloud provider. We have created a semantically rich data privacy ontology and integrated it with the properties of blockchain, to develop an automated access-control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. Our blockchain based data-sharing solution addresses two of the most critical challenges specifically: transaction verification and permissioned data obfuscation. Our solution ensures accountability for data sharing in cloud, by incorporating a secure and efficient system for End-to-End provenance. We concur that decisions regarding the collection, sharing, and use of Personally Identifiable Information (PII) must take into account both ethical and privacy considerations.Item Link before you share: Managing privacy policies through blockchain(IEEE, 2018-01-15) Banerjee, Agniva; Joshi, Karuna PandeWith the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and track the confidential information that they share with the providers. We have developed a novel framework to automatically track details about how a user's PII is stored, used and shared by the provider. We have integrated our data privacy ontology with the properties of blockchain, to develop an automated access-control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. We have also validated this framework by implementing a working system LinkShare. In this paper, we describe our framework on detail along with the LinkShare system. Our approach can be adopted by big data users to automatically apply their privacy policy on data operations and track the flow of that data across various stakeholders.