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This work aims to enable efficient digital rights management for volumetric video by introducing attribute-based selective coordinate encryption for point clouds. The method encrypts only a subset of coordinates to reduce computation and latency while maintaining security. Selective encryption makes point cloud frames distorted enough to block meaningful unauthorized viewing while still allowing basic visibility. The framework allows variation in the amount and type of encrypted coordinates (X, Y, Z, or combinations). Visual degradation is measured using standard point cloud quality metrics. Results show that encrypting only X coordinates reduces encryption time by 37% and decryption time by 46% compared to full encryption. Encrypting X and Y reduces these times by 20% and 36% while still degrading visual quality. Attribute-Based Encryption allows protected content to be cached and distributed without re-encryption, which reduces computation and latency. The current evaluation covers individual frames. Future work will cover full volumetric video streams and analyze caching gains during streaming with Attribute-Based Encryption. 

This paper presents an alternate method for encrypting video streams using attribute-based encryption, focusing on securing the data rather than the connection between streaming endpoints. This shift allows video segments to be encrypted once at the source and cached anywhere, removing the need for per-client encryption and decryption at intermediate caches. Access can be restricted or revoked by disabling users’ private keys instead of re-encrypting the entire video stream. The approach also removes the need for decryption and re-encryption at caches, since encrypted content can be stored directly. The work introduces ABEVS, a framework that applies attribute-based encryption to DRM-enabled video streaming. ABE is integrated into a DASH-based streaming client, and video content is encrypted and distributed through intermediate caches. The system is evaluated on CloudLab. Results show that the approach reduces computational load on caches and has minimal impact on cache hit rate and video quality compared to HTTPS-based streaming. 

In recent years, Field Programmable Gate Arrays (FPGAs) have gained prominence in cloud computing data centers, driven by their capacity to offload compute-intensive tasks and contribute to the ongoing trend of data center disaggregation, as well as their ability to be directly connected to the network. While FPGAs offer numerous advantages, they also pose challenges in terms of configuration, programmability, and monitoring, particularly in the absence of an operating system with essential features like the TCP/IP networking stack. This paper introduces an In-band Network Telemetry (INT) approach based on the P4 language for FPGA data plane programming. The goal is to facilitate monitoring and network performance analysis by providing one-way packet delay information. The approach is demonstrated in the Open Cloud Testbed (OCT) and FABRIC testbeds, both offering open access to the research community with greater FPGA availability than commercial clouds. The workflow enables researchers to create custom P4 programs and bitstreams for installation on FPGAs. The paper presents a multi-step approach allowing experimentation within the New England Research Cloud (NERC), testing in OCT, and final deployment in FABRIC, well-suited for one-way delay measurements due to synchronized clocks via GPS time signals. Contributions include the provision of a P4 workflow for FPGAs in a research cloud, a novel FPGA clock-based INT approach, and a comprehensive evaluation through simulation and experiments in the Open Cloud and FABRIC testbeds.