Privacy in Multi-Agent and Dynamical Systems / Fragkiskos Koufogiannis.

Format:

Book

Thesis/Dissertation

Author/Creator:

Koufogiannis, Fragkiskos, author.

Contributor:

Pappas, George J., degree supervisor.

Walker, Jesse, degree committee member.

Roth, Aaron, degree committee member.

Preciado, Victor M., degree committee member.

University of Pennsylvania. Electrical and Systems Engineering, degree granting institution.

Language:

English

Subjects (All):

Engineering.

Information technology.

Electrical and Systems Engineering--Penn dissertations.

Penn dissertations--Electrical and Systems Engineering.

Local Subjects:

Engineering.

Information technology.

Electrical and Systems Engineering--Penn dissertations.

Penn dissertations--Electrical and Systems Engineering.

Genre:

Academic theses.

Physical Description:

1 online resource (127 pages)

Contained In:

Dissertation Abstracts International 79-01B(E).

Place of Publication:

[Philadelphia, Pennsylvania]: University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2017.

Language Note:

English

System Details:

Mode of access: World Wide Web.

text file

Summary:

The use of private data is pivotal for numerous services including location--based ones, collaborative recommender systems, and social networks. Despite the utility these services provide, the usage of private data raises privacy concerns to their owners. Noise--injecting techniques, such as differential privacy, address these concerns by adding artificial noise such that an adversary with access to the published response cannot confidently infer the private data. Particularly, in multi--agent and dynamical environments, privacy--preserving techniques need to be expressive enough to capture time--varying privacy needs, multiple data owners, and multiple data users. Current work in differential privacy assumes that a single response gets published and a single predefined privacy guarantee is provided. This work relaxes these assumptions by providing several problem formulations and their approaches. In the setting of a social network, a data owner has different privacy needs against different users. We design a coalition--free privacy--preserving mechanism that allows a data owner to diffuse their private data over a network. We also formulate the problem of multiple data owners that provide their data to multiple data users. Also, for time--varying privacy needs, we prove that, for a class of existing privacy--preserving mechanism, it is possible to effectively relax privacy constraints gradually. Additionally, we provide a privacy--aware mechanism for time--varying private data, where we wish to protect only the current value of it. Finally, in the context of location--based services, we provide a mechanism where the strength of the privacy guarantees varies with the local population density. These contributions increase the applicability of differential privacy and set future directions for more flexible and expressive privacy guarantees.

Notes:

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Advisors: George J. Pappas; Committee members: Victor M. Preciado; Aaron Roth; Jesse Walker.

Department: Electrical and Systems Engineering.

Ph.D. University of Pennsylvania 2017.

Local Notes:

School code: 0175

ISBN:

9780355182873

Access Restriction:

Restricted for use by site license.