@InProceedings{SR19,
  author =       { Mayuri Sridhar and Ronald L. Rivest },
  title =        { k-Cut: A Simple Approximately-Uniform Method
                   for Sampling Ballots in Post-Election Audits },
  booktitle =    { Proceedings Voting'19 },
  date =         { 2019-02-22 },
  eventdate =    { 2019-02-22 },
  venue =        { St. Kitts },
  note =         { (Talk by Mayuri Sridhar.) },                  
  abstract =     { We present an approximate sampling framework and
                  discuss how risk-limiting audits can compensate for
                  these approximations, while maintaining their
                  "risk-limiting" properties. Our framework is general
                  and can compensate for counting mistakes made during
                  audits.  Moreover, we present and analyze a simple
                  approximate sampling method, "k-cut", for picking a
                  ballot randomly from a stack, without counting.  Our
                  method involves doing k "cuts," each involving
                  moving a random portion of ballots from the top to
                  the bottom of the stack, and then picking the
                  ballot on top. Unlike conventional methods of
                  picking a ballot at random, k-cut does not require
                  identification numbers on the ballots or counting
                  many ballots per draw.  We analyze how close the
                  distribution of chosen ballots is to the uniform
                  distribution, and design mitigation procedures. We
                  show that k = 6 cuts is enough for a risk-limiting
                  election audit, based on empirical data, which
                  provides a significant increase in sampling
                  efficiency. This method has been used in pilot RLAs
                  in Indiana and in Michigan
                  pilot audits in December 2018. },
}