TY - GEN
T1 - PrivacySphere
T2 - 6th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024
AU - Farrukh, Habiba
AU - Lahjouji, Nada
AU - Mehrotra, Sharad
AU - Nawab, Faisal
AU - Rousseau, Julie
AU - Sharma, Shantanu
AU - Venkatasubramanian, Nalini
AU - Yus, Roberto
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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).
AB - 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).
KW - data privacy regulations
KW - privacy
KW - smart spaces
UR - http://www.scopus.com/inward/record.url?scp=85217869806&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85217869806&partnerID=8YFLogxK
U2 - 10.1109/TPS-ISA62245.2024.00037
DO - 10.1109/TPS-ISA62245.2024.00037
M3 - Conference contribution
AN - SCOPUS:85217869806
T3 - Proceedings - 2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024
SP - 255
EP - 264
BT - Proceedings - 2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 October 2024 through 30 October 2024
ER -