Data Security in Precision Agriculture: How Machine Learning Will Exacerbate the Privacy Risks of Farming
Anna Johnson As precision agriculture technology advances, privacy concerns plague farmers who fear that their agricultural data could be exploited by data processors. Despite these concerns, the federal government has failed to enact agricultural data protection laws. With the rise of machine learning (ML), federal and state legislators should act urgently to prohibit AI-driven, farming data misuse. Since data security threats in agriculture can disrupt the stability of the commodities market, a coordinated, industry-specific approach ensuring the privacy of agricultural data must be taken. This paper analyzes the privacy risks created by ML in precision agriculture and proposes legislative and...
BLOG POST: The Golden State Killer and the Privacy Risks of Direct-to-Consumer Genetic Testing
*This writing is a blog post. It is not a published IPTF Journal article. Jordan Rose In 2018, a breakthrough in a 40-year-old unsolved case involving a series of homicides, sexual assaults, and burglaries, known as the Golden State Killer, shocked the public.[1] Law enforcement used an innovative, yet controversial, approach to identify the suspect.[2] Investigators uploaded an unidentified DNA sample from one of the original crime scenes to GEDMatch, an open-source genetic database specifically designed to provide consumers a method of identifying genetic relatives by uploading their DNA samples, rather than to help identify criminal suspects.[3] Over four months,...