PrivacySphere: Privacy-Preserving Smart Spaces

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

In smart spaces, data flows from sensors through data processing pipelines that interpret and enrich it to realize the needs of diverse applications. Smart space data may also be stored for future analysis and processing to implement new functionalities and learn correlations that can help improve deployed applications. Data processing may be performed at the edge (on sensors or at trusted local servers) or may be relegated to the (possibly untrusted) public cloud. This paper presents PrivacySphere, our vision towards a plug-n-play framework to integrate and test a variety of Privacy Enhancing Technologies (PETs) in smart spaces. PrivacySphere will support mechanisms (with appropriate APIs) to control when data is collected and from which sensors; in what format the data is exposed to devices/machines; and to whom (i.e., individuals/entities). Using PrivacySphere, flow of data may be intercepted at any point of the execution to apply PETs (e.g., differential privacy, encryption, policy-based sharing).

Identifier

85217869806 (Scopus)

ISBN

[9798350386745]

Publication Title

Proceedings - 2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

External Full Text Location

https://doi.org/10.1109/TPS-ISA62245.2024.00037

First Page

255

Last Page

264

Grant

2008993

Fund Ref

National Science Foundation

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