About the Project
The growing use of social-networking sites like Facebook and YouTube, along with technical advances in data-retrieval techniques, are providing new opportunities to make use of people’s personal information — and those opportunities are equally available for both ethical and unethical uses. Current computer-science curricula at high schools and colleges usually include an abundance of material on data-retrieval methods and how to improve them, but rarely make room for discussion of the potential negative impact of these technologies. Among the groups most affected by those negative impacts are high-school students; they are the most frequent users of social-networking sites and apps, but often do not have a full understanding of the potential consequences their current online activities might have later in their lives. For example, a Facebook posting that a high-schooler’s friends think is cool might be seen by a much larger audience than she expected — including perhaps future employers who wouldn’t think it was so cool. In addition, not understanding — or not thinking about — the consequences of posting often leads to over-sharing information about other people, including friends and relatives.
The Teaching Privacy project is an NSF-sponsored* collaboration between the International Computer Science Institute and the University of California-Berkeley. The project aims to empower K-12 students and college undergrads in making informed choices about privacy, by building a set of educational tools and hands-on exercises to help teachers demonstrate what happens to personal information on the Internet — and what the effects of sharing information can be.
For more information, drop us an e-mail at firstname.lastname@example.org.
This work was supported by funding provided to the International Computer Science Institute by the National Science Foundation, through grants CNS‐1065240 and DGE-1419319, and by the Broadband Technology Opportunities Program through the California Connects program. Additional support comes from funding provided to the University of California–Berkeley through NSF grants EEC-1405547 and CCF-0424422 and through the IISME Summer Fellowship Program.
* Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the individual authors or originators and do not necessarily reflect the views of the National Science Foundation, BTOP, IISME, other funders, the International Computer Science Institute, nor UC Berkeley.