@misc{SR18y,
    author = { Mayuri Sridhar and Ronald L. Rivest },
    title = { $k$-cut: A Simple Approximately-Uniform Method for Sampling
              Ballots in Post-Election Audits },
    date =  { 2018-11-21 },
    OPTyear = { 2018 },
    OPTmonth = { November 21, },
    urla =  { <a href="https://arxiv.org/abs/1811.08811">arXiv</a> },
    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 different mitigation
    procedures. We show that k=6 cuts is enough for an risk-limiting
    election audit, based on empirical data, which would provide
    a significant increase in efficiency." },
}