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B.J. Boëtius
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Encryption of Event Camera Data for Visual Localisation
How can the encryption of raw event camera data be practically and effectively used for privacy protection in a visual localisation application?
Event cameras are bio-inspired sensors that asynchronously measure per-pixel brightness changes, offering lower power consumption and higher temporal resolution than conventional frame cameras. These properties make them suitable for privacy-sensitive applications, such as visual localisation in AR/VR systems, where client-server architectures are used to offload computationally expensive processing from resource-limited edge devices. However, transmitting visual data to a service provider introduces privacy risks. Kim et al. propose a privacy-preserving visual localisation method that assumes an honest-but-curious service provider, but acknowledge that their approach is insufficient against a more capable attacker that can, for example, extract raw event data directly. This paper addresses this limitation by encrypting raw event camera data using the algorithm described by Zhang et al., for which no implementation was previously available. The algorithm is implemented within the visual localisation pipeline of Kim et al. and evaluated on the EvRooms dataset. The theoretical and practical effectiveness of the encryption is analysed, and improvements to the original algorithm are proposed and tested. The impact on both privacy preservation and localisation performance is measured. The paper shows that the polarity-mapping step in the implemented encryption algorithm is a powerful event data obfuscation process while still allowing retrieval of the original data. However, this process is currently not dependent on a key, which makes the algorithm not secure according to Kerckhoffs's principle. Further research should explore encryption algorithms that employ key-dependent polarity mapping. The code used in this research can be found on GitHub.
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Event cameras are bio-inspired sensors that asynchronously measure per-pixel brightness changes, offering lower power consumption and higher temporal resolution than conventional frame cameras. These properties make them suitable for privacy-sensitive applications, such as visual localisation in AR/VR systems, where client-server architectures are used to offload computationally expensive processing from resource-limited edge devices. However, transmitting visual data to a service provider introduces privacy risks. Kim et al. propose a privacy-preserving visual localisation method that assumes an honest-but-curious service provider, but acknowledge that their approach is insufficient against a more capable attacker that can, for example, extract raw event data directly. This paper addresses this limitation by encrypting raw event camera data using the algorithm described by Zhang et al., for which no implementation was previously available. The algorithm is implemented within the visual localisation pipeline of Kim et al. and evaluated on the EvRooms dataset. The theoretical and practical effectiveness of the encryption is analysed, and improvements to the original algorithm are proposed and tested. The impact on both privacy preservation and localisation performance is measured. The paper shows that the polarity-mapping step in the implemented encryption algorithm is a powerful event data obfuscation process while still allowing retrieval of the original data. However, this process is currently not dependent on a key, which makes the algorithm not secure according to Kerckhoffs's principle. Further research should explore encryption algorithms that employ key-dependent polarity mapping. The code used in this research can be found on GitHub.