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We study the ON-OFF privacy problem. At each time, the user is interested inthe latest message of one of $N$ sources. Moreover, the user is assumed to beincentivized to turn privacy ON or OFF whether he/she needs it or not. Whenprivacy is ON, the user wants to keep private which source he/she is interestedin. The challenge here is that the user's behavior is correlated over time.Therefore, the user cannot simply ignore privacy when privacy is OFF, becausethis may leak information about his/her behavior when privacy was ON due tocorrelation.  We model the user's requests by a Markov chain. The goal is to design ON-OFFprivacy schemes with optimal download rate that ensure privacy for past andfuture requests. The user is assumed to know future requests within a window ofpositive size $\omega$ and uses it to construct privacy-preserving queries. Inthis paper, we construct ON-OFF privacy schemes for $N=2$ sources and provetheir optimality.

Preserving ON-OFF Privacy for Past and Future Requests