WG-29 is a Welch-Gong (WG) stream cipher, implemented in$GF (2^{29})$and an 11-stage LFSR, whose polynomial-basis (PB)-based architecture is utilized in diverse applications. This work, for the first time, presents low-cost normal signature, interleaved signature, and Hamming code-based error detection mechanisms for the hardware implementations of PB-based WG-29 stream cipher. The presented schemes are benchmarked on field-programmable gate array (FPGA) hardware platform using Kintex-7 and Spartan-7 FPGA families for area$(< 40\%)$, power$(< 12\%)$, and delay$(< 10\%)$overheads. Using a faulty module to inject stuck-at single bit and multiple bit upsets, the error coverage for these presented schemes is evaluated via simulations performed in Xilinx Vivado for 80 000 faults and shown to be over 99.99%. The overhead and error simulation results for the presented schemes show that they provide high-error coverage with acceptable overheads to make hardware constructions of WG-29 more reliable. Other WG ciphers that have similar underlying primitives can also benefit from the presented work, with slight modifications, for secure hardware implementations.

Privacy policies are statements about how websites, applications, and any other service providers collect, use, share and manage users' data. Nowadays, the contents of privacy policies have been affected by different regulations such as the General Data Protection Regulation (GDPR), which enforces the protection of personal data and also requires privacy policies to be more transparent for readers. There is a limited understanding of how GDPR has impacted the content of privacy policies. This study presents a framework for evaluation of compliance of privacy policies with GDPR recommendations and best practices. This evaluation framework includes text feature analysis, coverage analysis, and content analysis. Our findings suggest that although GDPR enforcement has improved the content of privacy policies, many of these privacy policies do not fully satisfy GDPR requirements.

Privacy policy is a legal document in which the users are informed about the data practices used by the organizations. Past research indicates that the privacy policies are long and hard to understand. They are also known to have incomplete content. Users are not inclined to read the policy as they have to read long policies to find information about data practices of an organization. The solution that we are proposing in this research is to assist users with finding relevant content to their queries using semantic approach. This thesis presents the development of domain ontology for privacy policies. Natural Language Processing was used to understand the content of the policies and capture vocabulary for the ontology. This vocabulary was further used to build the ontology so that the ontology highlights relevant sentences related to a privacy concern. We validated and evaluated the ontology using different methods: competency questions, data driven, metric based and user evaluation. Results from the evaluation of ontology show that the amount of text to read is significantly reduced as the users have to only read selected text that ranged from 1% to 30% of a privacy policy. The amount of text depended on the query and its associated keywords. This signifies that the time required to read a policy is significantly reduced as the ontology directs user to the right content for a query. This finding was also confirmed by the results of the user study session. The results from the user study session indicated that the users found ontology helpful in finding relevant selected sentences to read as compared to reading the entire policy.