Which technique specifically increases privacy in data analysis?

The technique that specifically increases privacy in data analysis is data aggregation. This method involves compiling data from multiple sources and summarizing it in a way that obscures individual details while still providing meaningful insights. By aggregating data, sensitive information about individual subjects is less likely to be exposed, thus enhancing privacy.

For instance, when analyzing data on consumer behavior, aggregating the data might mean reporting on average spending habits within a demographic rather than showing transactions on an individual level. This aggregation process masks personal identities and specific behaviors, making it difficult to trace back findings to any one person.

In contrast, imputation refers to filling in missing values in a dataset, which does not inherently focus on privacy; data reduction deals with simplifying or eliminating parts of the data without a direct emphasis on privacy; and data profiling involves analyzing a dataset’s structure and content but does not actively enhance privacy. Therefore, data aggregation stands out as the technique that specifically aims to protect individual privacy while still allowing for effective analysis of trends and patterns within the data.