In this paper, we address the problem of processing reverse top-k queries in a parallel and distributed setting. Given a database of objects, a set of user preferences, and a query object q, the reverse top-k query returns the subset of user preferences for which the query object belongs to the top-k results. Although recently, the reverse top-k query operator has been studied extensively, its CPU-intensive nature results in prohibitively expensive processing cost, when applied on vast-sized data sets. This limitation motivates us to explore a parallel processing solution, to enable reverse top-k query evaluation over GBs of data in reasonable execution time. To the best of our knowledge, this is the first work that addresses the problem of parallel reverse top-k query processing. We propose a solution to this problem, called DiPaRT, which is based on MapReduce and is provably correct. DiPaRT is empirically evaluated using GB-sized data sets.