Fig 7. Cryptosets provide a better tradeoff between estimate error and security risk.

The figures plot the average over 50 simulations of the overlap estimate error (on a log scale) against average information gain (in bits) for cryptosets and Bloom filters over a large range of lengths (ranging from 500 to 10000). The ideal method would have both error and information gain close to zero. The left column of figures uses datasets with 100 items, while the right column of uses datasets with 1000 items. The top row simulates these datasets with an overlap of 40%, while the bottom row simulates with an overlap of 80%. Cryptosets are consistently more accurate and secure than Bloom filters, and this trend is most pronounced in large datasets with lower overlaps (top right).