Abstract
The goal of this quantitative study is to identify, analyze, and forecast trends related to data protection and encryption technologies. Based on Wikipedia pageview statistics, we study the public interest in 36 technologies that were previously identified by field experts using the Delphi method. We explore the relationships between those technologies by measuring, analyzing, and classifying the time-varying attention (proxied by pageviews) given to each technology. Next, we predict these time series using different forecasting models. As the baseline for our models, we use open datasets, such as arXiv, Google Trends, and Twitter hashtags. Through our pre-analysis of the 36 technologies, we note different implications. We find that 12 technologies show a positive trend, in alignment with an increase of public interest in well-known growing technologies, such as “Blockchain”, “Homomorphic encryption” and “Zero-knowledge proof”. We also notice that only 5 technologies hold stable public interest, such as “Electronic Voting” and 19 technologies show a negative trend, indicating a decrease in public interest in well-established technologies, such as “Hash function”, “Email encryption” and “ Database encryption”. Information resources on technology and innovation are often extracted from very specific and scientific data. Here, we study technologies from a wide range of sources, including the general public, which allows us to have a peripheral view of the maturity, the dynamics and current status of each of the selected technologies. Altogether, our findings expand the TechMining literature by providing insight into the public interest in data protection and encryption technologies.