Search Results for « Privacy »

<p dir="ltr">Quantum technology is set to revolutionize the way we interact with a variety of different industries- disrupting people, processes, and other technologies at an exponential rate. As of now, the majority of our trusted encryption methods, such as Rivest-Shamir-Adleman (RSA), Elliptic Curve Digital Signature Algorithm (ECDSA), and Diffie-Hellman are not quantum-resistant. Therefore, a malicious actor in possession of a powerful enough quantum computer will be able to break encryption on files that may include sensitive data with ease in minimal time. This thesis provides insight on the current privacy landscape in quantum literature and demonstrates the existing research gap that requires further research contributions via a systematic literature review using two NLP techniques for keyword extraction. A taxonomy is produced as an outcome of a systematic review on 61 papers published between 2003 and 2023 with at least 5 citations, using two different natural language processing methods, deep learning and NVIVO.</p>

<p dir="ltr">Quantum technology is set to revolutionize the way we interact with a variety of different industries- disrupting people, processes, and other technologies at an exponential rate. As of now, the majority of our trusted encryption methods, such as Rivest-Shamir-Adleman (RSA), Elliptic Curve Digital Signature Algorithm (ECDSA), and Diffie-Hellman are not quantum-resistant. Therefore, a malicious actor in possession of a powerful enough quantum computer will be able to break encryption on files that may include sensitive data with ease in minimal time. This thesis provides insight on the current privacy landscape in quantum literature and demonstrates the existing research gap that requires further research contributions via a systematic literature review using two NLP techniques for keyword extraction. A taxonomy is produced as an outcome of a systematic review on 61 papers published between 2003 and 2023 with at least 5 citations, using two different natural language processing methods, deep learning and NVIVO.</p>

This thesis investigates the privacy risks that m-learning app users face by identifying the personal information that m-learning apps collect from their users, and the privacy policies of these apps. It reveals that most of the m-learning applications have similar privacy policies, which seem to protect the interest of the providers rather than the users. The Privacy by Design framework is reviewed to determine whether it can help the developers address user privacy practices. The results from the sample of 260 participants suggest that users are less concerned with the collection of personal information that is non-identifiable. The survey also revealed that the users are more concerned when an app shares their personal information with third parties for commercial purposes than when it is shared with the government.