ON-OFF Privacy with Correlated Requests

@article{Naim2019ONOFFPW,
  title={ON-OFF Privacy with Correlated Requests},
  author={Carolina Naim and Fangwei Ye and Salim Y. El Rouayheb},
  journal={2019 IEEE International Symposium on Information Theory (ISIT)},
  year={2019},
  pages={817-821}
}
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… 

Figures and Tables from this paper

ON-OFF Privacy in the Presence of Correlation

We formulate and study the problem of ON-OFF privacy. ON-OFF privacy algorithms enable a user to continuously switch his privacy between ON and OFF. An obvious example is the incognito mode in

ON-OFF Privacy Against Correlation Over Time

A polynomial-time algorithm is devised to construct an ON-OFF privacy scheme with optimal download rate that ensure privacy for past and future requests and an upper bound on the achievable rate is presented.

Optimal Local Bayesian Differential Privacy over Markov Chains

This paper improves on the state-of-the-art BDP mechanism and shows that the mechanism provides the optimal noise-privacy tradeoffs for any local mechanism up to negligible factors.

Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy

An informationtheoretic mechanism for masking sensitive genotypes is developed, which ensures no information about the sensitive genotype is leaked and an efficient algorithmic implementation of the mechanism for genomic data governed by hidden Markov models is proposed.

Preserving ON-OFF Privacy for Past and Future Requests

The goal is to design ON-OFF privacy schemes with optimal download rate that ensure privacy for past and future requests that are constructed for N sources and prove their optimality.

Advances and Open Problems in Federated Learning

Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

References

SHOWING 1-9 OF 9 REFERENCES

Private information retrieval with side information: The single server case

This work proves that, in the first scenario, the minimum download cost is K-M messages, and in the second scenario, it is ⌈K/M+1⌉ messages, a significant improvement compared to the minimum cost of K messages in the setting where the user has no side information.

k-Anonymity: A Model for Protecting Privacy

  • L. Sweeney
  • Computer Science
    Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 2002
The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment and examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected.

One extra bit of download ensures perfectly private information retrieval

This paper designs an explicit erasure code and PIR algorithm that requires only one extra bit of download to provide perfect privacy, and establishes the precise capacity of PIR with respect to the metric of download.

Private information retrieval from MDS coded data in distributed storage systems

This work considers the problem of providing privacy, in the private information retrieval (PIR) sense, to users requesting data from a distributed storage system (DSS), and constructs PIR schemes with low download communication cost, achieving the information theoretic limit for linear schemes.

Differential Privacy

  • C. Dwork
  • Computer Science
    Encyclopedia of Cryptography and Security
  • 2006
A general impossibility result is given showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved, which suggests a new measure, differential privacy, which, intuitively, captures the increased risk to one's privacy incurred by participating in a database.

The Capacity of Private Information Retrieval

  • Hua SunS. Jafar
  • Computer Science
    2016 IEEE Global Communications Conference (GLOBECOM)
  • 2016
A remarkable feature of the capacity achieving scheme is that if it is projected onto any subset of messages by eliminating the remaining messages, it also achieves the PIR capacity for that subset of message.

Private information retrieval

Schemes that enable a user to access k replicated copies of a database and privately retrieve information stored in the database and get no information on the identity of the item retrieved by the user are described.

Private Information Retrieval from Coded Databases with Colluding Servers

We present a general framework for Private Information Retrieval (PIR) from arbitrary coded databases, that allows one to adjust the rate of the scheme according to the suspected number of colluding

The Capacity of Private Information Retrieval From Coded Databases

The information-theoretic capacity of the PIR problem is derived, which is defined as the maximum number of bits of the desired message that can be privately retrieved per one bit of downloaded information.