ON-OFF Privacy with Correlated Requests
ON-OFF Privacy with Correlated Requests
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具有相关请求的开关隐私
DOI：
10.1109/isit.2019.8849461
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. E. Rouayheb
中科院分区：
文献类型：
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作者：
C. Naim;Fangwei Ye;S. E. Rouayheb
我们引入了ON-OFF隐私问题。每次，用户对随机选择的N个在线源中的一个的最新消息感兴趣，并且对于每个请求，他的隐私状态可以是ON或OFF。只有当隐私处于ON时，用户才想隐藏他感兴趣的源。问题是设计具有最大下载速率的ON-OFF隐私方案，允许用户私下获得他所请求的消息。在许多现实场景中，用户的请求是相关的，因为它们取决于他的个人属性，如年龄，性别，政治观点或地理位置。因此，即使当隐私是关闭的，他不能简单地透露他的请求，因为这将泄漏有关他的请求的信息时，隐私是ON。我们研究的情况下，当用户的请求可以建模的马尔可夫链和N = 2的来源。在这种情况下，我们提出了一个ON-OFF隐私方案并证明了其最优性。
We introduce the ON-OFF privacy problem. At each time, the user is interested in the latest message of one of N online sources chosen at random, and his privacy status can be ON or OFF for each request. Only when privacy is ON the user wants to hide the source he is interested in. The problem is to design ON-OFF privacy schemes with maximum download rate that allow the user to obtain privately his requested messages. In many realistic scenarios, the user’s requests are correlated since they depend on his personal attributes such as age, gender, political views, or geographical location. Hence, even when privacy is OFF, he cannot simply reveal his request since this will leak information about his requests when privacy was ON. We study the case when the users’s requests can be modeled by a Markov chain and N = 2 sources. In this case, we propose an ON-OFF privacy scheme and prove its optimality.