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Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Securely Storing and Sharing Memory Cues in Memory Augmentation Systems
T2 - 2019 IEEE International Conference on Pervasive Computing and Communications
AU - Bexheti, Agon
AU - Langheinrich, Marc
AU - Elhart, Ivan
AU - Davies, Nigel Andrew Justin
N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2019/3/11
Y1 - 2019/3/11
N2 - A plethora of sensors embedded in wearable, mobile, and infrastructure devices allow us to seamlessly capture large parts of our daily activities and experiences. It is not hard to imagine that such data could be used to support human memory in the form of automatically generated memory cues, e.g., images, that help us remember past events. Such a vision of pervasive “memory-augmentation systems”, however, comes with significant privacy and security implications, chief among them the threat of memory manipulation: without strong guarantees about the provenance of captured data, attackers would be able to manipulate our memories by deliberately injecting, removing, or modifying captured data. This work introduces this novel threat of human memory manipulation in memory augmentation systems. We then present a practical approach that addresses key memory manipulation threats by securing the captured memory streams. Finally we report evaluation results on a prototypical secure camera platform that we built.
AB - A plethora of sensors embedded in wearable, mobile, and infrastructure devices allow us to seamlessly capture large parts of our daily activities and experiences. It is not hard to imagine that such data could be used to support human memory in the form of automatically generated memory cues, e.g., images, that help us remember past events. Such a vision of pervasive “memory-augmentation systems”, however, comes with significant privacy and security implications, chief among them the threat of memory manipulation: without strong guarantees about the provenance of captured data, attackers would be able to manipulate our memories by deliberately injecting, removing, or modifying captured data. This work introduces this novel threat of human memory manipulation in memory augmentation systems. We then present a practical approach that addresses key memory manipulation threats by securing the captured memory streams. Finally we report evaluation results on a prototypical secure camera platform that we built.
U2 - 10.1109/PERCOM.2019.8767389
DO - 10.1109/PERCOM.2019.8767389
M3 - Conference contribution/Paper
SN - 9781538691496
BT - 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom)
PB - IEEE
Y2 - 11 March 2019 through 15 March 2019
ER -