Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Seeding with Differentially Private Network Information

Rahimian, M Amin and Yu, Fang-Yi and Hurtado, Carlos Seeding with Differentially Private Network Information.

[img] Archive (ZIP) (Github archive)
Primary Text

Download (13kB)
[img] Plain Text (licence)
Download (1kB)


When designing interventions in public health, development, and education, decision makers rely on social network data to target a small number of people, capitalizing on peer effects and social contagion to bring about the most welfare benefits to the population. Developing new methods that are privacy-preserving for network data collection and targeted interventions is critical for designing sustainable public health and development interventions on social networks. In a similar vein, social media platforms rely on network data and information from past diffusions to organize their ad campaign and improve the efficacy of targeted advertising. Ensuring that these network operations do not violate users' privacy is critical to the sustainability of social media platforms and their ad economies. We study privacy guarantees for influence maximization algorithms when the social network is unknown, and the inputs are samples of prior influence cascades that are collected at random. Building on recent results that address seeding with costly network information, our privacy-preserving algorithms introduce randomization in the collected data or the algorithm output, and can bound each node's (or group of nodes') privacy loss in deciding whether or not their data should be included in the algorithm input. We provide theoretical guarantees of the seeding performance with a limited sample size subject to differential privacy budgets in both central and local privacy regimes. Simulations on synthetic and empirical network datasets reveal the diminishing value of network information with decreasing privacy budget in both regimes.


Social Networking:
Share |


Item Type: Other
CreatorsEmailPitt UsernameORCID
Rahimian, M AminRAHIMIAN@pitt.eduRAHIMIAN0000-0001-9384-1041
Yu, Fang-Yi
Hurtado, Carlos
Schools and Programs: Swanson School of Engineering > Industrial Engineering
Refereed: No
Uncontrolled Keywords: cs.SI, cs.SI, cs.CC, cs.MA, math.PR, stat.AP, 91D30, 05C80
Additional Information: Preliminary version in AAMAS 2023: -- Code and data:
Date Deposited: 12 Jun 2023 17:32
Last Modified: 07 Feb 2024 20:55


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item