Abstract. Many organizations such as the U.S. Census publicly release samples of data that they collect about private citizens. These datasets are first anonymized using various techniques and then a small sample is released so as to enable "do-it-yourself" calculations. This paper investigates the privacy of the second step of this process: sampling. We observe that rare values -values that occur with low frequency in the table -can be problematic from a privacy perspective. To our knowledge, this is the first work that quantitatively examines the relationship between the number of rare values in a table and the privacy in a released random sample. If we require -privacy (where the larger is, the worse the privacy guarantee) with probability at least 1 − δ, we say that a value is rare if it occurs in at mostÕ( 1 ) rows of the table (ignoring log factors). If there are no rare values, then we establish a direct connection between sample size that is safe to release and privacy. Specifically, if we select each row of the table with probability at most then the sample is O( )-private with high probability. In the case that there are t rare values, then the sample isÕ( δ/t)-private with probability at least 1 − δ.
Chaudhuri and Mishra REF studied the privacy effect of sampling, and showed a linear relationship between the sampling probability and the error probability δ.
When Random Sampling Preserves Privacy
CRYPTO
Computer Science