@InProceedings{WangHMD:CDC2014,
  Title                    = {Entropy-minimizing Mechanism for Differential Privacy of Discrete-time Linear Feedback Systems},
  Author                   = {Yu Wang and Zhenqi Huang and Sayan Mitra and Geir Dullerud},
  Booktitle                = {Conference on Decision and Control (CDC)},
  Year                     = {2014},
  Publisher                = {IEEE},

  Abstract                 = {The concept of differential privacy stems from the study of private query of datasets. In this work, we apply this concept to metric spaces to study a mechanism which randomizes a deterministic query by adding mean-zero noise to keep differential privacy. For one-shot queries, we show that \dpc{} of an $n$-dimensional input implies a lower bound $n + n \ln(2 \epsilon)$ on the entropy of the randomized output, and this lower bound is achieved by \Ln. We then consider the \dpc{} of a discrete-time linear feedback system in which noise is added to the system output at each time. The adversary, modeled as a filter, estimates the system states from the output history. We show that, to keep the system \dpt{}, the output entropy is bounded below, and this lower bound is achieves by an explicit mechanism.},
  Keywords                 = {Security and Privacy},
  Owner                    = {mitras},
  Pdfurl                   = {research/2014/privacy_entropy.pdf},
  Timestamp                = {2014.10.24},
  Url                      = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7039713}
}