A privacy framework

Jinfei Liu, Li Xiong, Jun Luo
<span title="">2013</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/25kc37fuhndb5axzv75226fwem" style="color: black;">Proceedings of the Joint EDBT/ICDT 2013 Workshops on - EDBT &#39;13</a> </i> &nbsp;
In this paper we illustrate a privacy framework named Indistinguishable 1 Privacy. Indistinguishable privacy could be deemed as the formalization of the existing privacy definitions in privacy preserving data publishing as well as secure multi-party computation. We introduce three variants of the representative privacy notions in the literature, Bayes-optimal privacy for privacy preserving data publishing, differential privacy for statistical data release, and privacy w.r.t. semi-honest
more &raquo; ... in the secure multi-party computation setting, and prove they are equivalent. To the best of our knowledge, this is the first work that illustrates the relationships of these privacy definitions and unifies them through one framework. number of participants wish to compute a public function on their private inputs without disclosing the private inputs to each other. With decades' development, there are three essential privacy principles in the literature on PPDP and SMC: Bayes-optimal privacy [24] in the PPDP setting, and differential privacy [6] in the PPDP setting, and privacy w.r.t. semi-honest behavior [12] in the SMC setting.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2457317.2457340">doi:10.1145/2457317.2457340</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/edbt/LiuXL13.html">dblp:conf/edbt/LiuXL13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ah33u74vz5f2xa7nwk2f7rwvsq">fatcat:ah33u74vz5f2xa7nwk2f7rwvsq</a> </span>
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