Database driven dynamic spectrum sharing is one of the most promising dynamic spectrum access (DSA) solution to address the spectrum scarcity issue. In such a database driven DSA system, the centralized spectrum management infrastructure, called spectrum access system (SAS), makes its spectrum allocation decisions to secondary users (SUs) according to sensitive operational data of incumbent users (IUs). Since both SAS and SUs are not necessarily fully trusted, privacy protection against untrusted SAS and SUs become critical for IUs that have high operational privacy requirements. To address this problem, many IU privacy preserving solutions emerge recently. However, there is a lack of understanding and comparison of capability in protecting IU operational privacy under these existing approaches. In this paper, thus, we fill in the void by providing a comparative study that investigates existing solutions and explores several existing metrics to evaluate the strength of privacy protection. Moreover, we propose two general metrics to evaluate privacy preserving level and evaluate existing works with them.
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PReVer: Towards Private Regulated Verified Data
Data privacy has garnered significant attention recently due to diverse applications that store sensitive data in untrusted infrastructure. From a data management point of view, the focus has been on the privacy of stored data and the privacy of querying data at a large scale. However, databases are not solely query engines on static data, they must support updates on dynamically evolving datasets. In this paper, we lay out a vision for privacy-preserving dynamic data. In particular, we focus on dynamic data that might be stored remotely on untrusted providers. Updates arrive at a provider and are verified and incorporated into the database based on predefined constraints. Depending on the application, the content of the stored data, the content of the updates and the constraints may be private or public. We then propose PReVer, a universal framework for managing regulated dynamic data in a privacy-preserving manner. We explore a set of research challenges that PReVer needs to address in order to guarantee the privacy of data, updates, and/or constraints and address the consistent and verifiable execution of updates. This opens the space of privacy-preserving data management from the narrow perspective of private queries on static datasets to the larger space of private management of dynamic data.
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- Award ID(s):
- 1703560
- NSF-PAR ID:
- 10331213
- Date Published:
- Journal Name:
- Proceedings of the 25th International Conference on Extending Database Technology, (EDBT'2022)
- Page Range / eLocation ID:
- 454-461
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
