Privacy concerns are a major factor when conducting research on human subjects. In addition, the advent of social media and advances in cellular technology have enabled IT sectors to easily gather large volumes of personal data for business research. Recently, revelations of the breadth of these data collections have led to a greater desire for user anonymity. Most current anonymization software, however, lacks flexibility, preventing users with different security preferences from adjusting their privacy settings. This complexity has made it difficult to gather useful data and discourages users from offering their usage information. This technology provides an alternative technique to anonymity, allowing researchers to collect more useful data while providing users with greater personalized safety and comfort with respect to their Internet identities.
Big data gathered from social networking sites and the like often require some anonymization technology for user privacy. However, different individuals have varying levels of comfort towards certain types of data collection. Identifying and adjusting privacy settings to accommodate user needs encourages individual enrollment and data sharing. Unfortunately, despite this demand, most available anonymization techniques lack such adaptability. This technology allows users to configure their anonymity as they desire, making it fully adaptable for each individual. The proposed algorithm provides comfort and safety to the user with additional variables that increase data security while also improving the quality and quantity of data collected. Additionally, this adaptable anonymization allows for enhanced control of knowledge loss, a characteristic typical of data mining approaches.
This algorithm was tested on benchmark and social data sets.
Patent Pending (US 2016292455)
Tech Ventures Reference: IR CU14173