ON-OFF Privacy with Correlated Requests
By Carolina Naim, Fangwei Ye, Salim el Rouayheb. 2019
9 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Sociology and Political Science, Privacy & Security
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Martin Jaggi
- Qiang Yang
- Jakub Konecný
- Peter Kairouz
- Peter Richtárik
- Ananda Theertha Suresh
- Jianyu Wang
- Sebastian U. Stich
In Sociology and Political Science
- Aurélien Bellet
- Salim el Rouayheb
- Peter Kairouz
- Rasmus Pagh
- Aleksandra Korolova
- Justin Hsu
- Carolina Naim
- Fangwei Ye
In Privacy & Security
- Peter Kairouz
- Badih Ghazi
- Rasmus Pagh
- Qiang Yang
- Ziteng Sun
- Adrià Gascón
- H. Brendan McMahan
- Aurélien Bellet
Citations in the corpus
- Advances and Open Problems in Federated Learning (2019, background): “PIR has also been studied in the context of ON-OFF privacy, in which a client is permitted to switch off their privacy guards in exchange for better utility or performance [306, 423].”
- Preserving ON-OFF Privacy for Past and Future Requests (2019, methodology): “We follow a setup similar to the one in [5] in which the user’s request are modeled by a Markov chain, but with one significant difference.”