Speaker: Christian Klein TITLE: Unbiased Controlled Rounding ABSTRACT: Rounding a real-valued matrix to an integer one, such that the rounding errors in all rows and columns are less than one, is a classical problem. It has been applied to hyper-graph coloring, in scheduling and in statistics. Here, it often is also desirable to round each entry randomly such that the probability of rounding it up equals its fractional part. This is known as unbiased rounding in statistics and as randomized rounding in computer science. In statistics, such roundings are mainly used for two reasons. First, given a table of statistical data, one can increase its readability by rounding each entry to multiples of (some power of) 10. Second, such roundings can be used for statistical disclosure control. Here, one wants to avoid that an attacker can identify individual respondents from the published data. In this talk, several ways to compute such roundings are presented. They all also ensure some kind of local data consistency that is particularly useful for statistical data, namely that the rounding error in all initial intervals of rows (and columns) is less than one.