Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data
Various Internet of Things (IoT) devices generate complex, dynamically changed, and infinite data streams. Adversaries can cause harm if they can access the user’s sensitive raw streaming data. For this reason, protecting the privacy of the data streams is crucial. In this paper, we explore local differential privacy techniques for streaming data. We compare the techniques and report the advantages and limitations. We also present the effect on component (e.g., smoother, perturber) variations of distribution-based local differential privacy. We find that combining distribution-based noise during perturbation provides more flexibility to the interested entity.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1841488
- Resource Relation:
- Conference: 18th Annual International Conference on Privacy, Security and Trust (PST2021) - Auckland, New Zealand/ Virtual Conference, , New Zealand - 12/13/2021 3:00:00 PM-12/15/2021 3:00:00 PM
- Country of Publication:
- United States
- Language:
- English
Similar Records
Analyzing Data Privacy for Edge Systems
Investigating Users’ Privacy Concerns of Internet of Things (IoT) Smart Devices
Analyzing Insider Risk Threat to the Internet of Things (IoT)
Conference
·
Wed Jun 01 00:00:00 EDT 2022
·
OSTI ID:1841488
Investigating Users’ Privacy Concerns of Internet of Things (IoT) Smart Devices
Conference
·
Sat Oct 01 00:00:00 EDT 2022
·
OSTI ID:1841488
Analyzing Insider Risk Threat to the Internet of Things (IoT)
Conference
·
Mon May 01 00:00:00 EDT 2023
·
OSTI ID:1841488