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REVIEW 3 major objections 3 minor 49 references

ABE-VVS: Attribute-Based Encrypted Volumetric Video Streaming

T0 review · 3 major / 3 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read Selectively encrypting only the X coordinates of a point-cloud video frame distorts the scene enough to block unauthorized viewing, while cutting encryption time up to 50% and decryption up to 80% versus full-frame encryption.

desk verdict Plausible streaming/CPU evaluation, but the DRM claim dies on the paper's own output: normals left in the clear let an attacker reconstruct X analytically. read the letter →

arxiv 2601.08987 v2 pith:L242ISDX submitted 2026-01-13 cs.CR cs.MMcs.NIeess.IV

Mohammad Waquas Usmani , Susmit Shannigrahi , Michael Zink This is my paper · ORCID
classification cs.CRcs.MMcs.NIeess.IV
keywords pointcloudsvolumetricvideoattribute-basedencryptionselectivedigitalrightsmanagement6DoFstreamingperformancecoordinateobfuscation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

ABE-VVS tries to establish that point-cloud volumetric video—the six-degrees-of-freedom (6DoF) format behind modern AR/VR—can be protected with a lightweight, attribute-based DRM layer: instead of encrypting whole frames (or whole TLS sessions), the content owner encrypts just selected spatial coordinates, by default only the X values, within each point-cloud frame. The paper argues and measures that this still flattens the geometry so thoroughly that a zero-filling unauthorized viewer cannot make out the scene, while the reduced ciphertext payload cuts encryption and decryption costs by up to 50% and 80% versus full-frame encryption. In an end-to-end streaming evaluation, the ABE-over-HTTP pipeline keeps server CPU load about 80% lower than HTTPS and cache CPU about 63% lower, with cache hit rates and rebuffering comparable to unsecured HTTP—because caches can store pre-encrypted frames without per-client TLS work. The result matters because 6DoF volumetric video is far more data-heavy than 2D or 360-degree video, so any DRM that forces per-client encryption at the server or adds seconds of decryption on head-mounted displays is impractical. If these measurements hold, ABE-X is a candidate DRM primitive for volumetric streaming that scales through CDNs rather than against them.

What carries the argument

The load-bearing mechanism is the selective coordinate encryption pattern P (e.g., X, XY, XYZ, 2X): the encoder strips chosen coordinate fields from each vertex into one ABE-encrypted buffer, leaving normals, colors, and header in plaintext; decryption restores them in-place to reconstruct the frame. The pattern determines both obfuscation and cost, and ABE's ciphertext-policy property—one public-key encryption usable by any key whose attributes satisfy the policy—is what lets the same pre-encrypted frame be cached and served to many clients without per-request encryption or TLS termination at intermediaries.

What would settle it

Apply a point-cloud completion network (or simple surface-continuity interpolation) to an X-only encrypted frame, where Y, Z, normals, and colors are known; reconstruct X and render the result to human raters, or compare Chamfer/Hausdorff distance against the original. If reconstructing recognizable geometry succeeds—e.g., distances drop to levels comparable to 2X zero-fill, or the rendered scene is identifiable—the 'prevents meaningful unauthorized viewing' claim is falsified.

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Extended reading notes

Core claim

The central claim is that selective coordinate encryption under attribute-based encryption gives volumetric point-cloud video a practical DRM layer. The algorithm removes targeted coordinates (X, Y, Z, or every nth X) from each vertex, packs them into a buffer, encrypts that buffer under an ABE access policy, and appends it to the frame; authorized clients decrypt and reinsert the values, while unauthorized viewers see either missing or zeroed coordinates. The paper is explicit that the goal is substantial visual obfuscation, not full cryptographic privacy: zeroing all X values makes chairs and other objects unrecognizable, which is enough to prevent meaningful unauthorized viewing. Quantita

Load-bearing premise

The claim that ABE-X 'prevents meaningful unauthorized viewing' assumes an attacker's best play is to zero-fill the missing X coordinates; the paper never measures whether an attacker can recover X from the still-revealed Y, Z, normals, and colors via surface-continuity interpolation or a learned completion model.

Editorial extensions

If this is right

  • If ABE-X obfuscation holds, volumetric DRM can be offered at roughly the cost of an unencrypted HTTP pipeline at servers and caches, changing the economics of securing 6DoF content.
  • Since caches store ABE ciphertext directly, CDN-style distribution no longer requires TLS termination or re-encryption at every hop; encrypted content can be replicated and cached as plain bytes.
  • Selective encryption gives operators a tunable knob: full XYZ for high-value content, X-only for cheaper protection, with granularities below X documented as providing negligible obfuscation gains at no extra speedup.
  • The roughly 580-byte ABE overhead per frame means the DRM layer adds little to streaming bitrate, so existing point-cloud streaming systems could adopt it without manifest or bandwidth redesigns.
  • Because ABE supports time-based attributes, key expiry can revoke a subscriber's access without re-encrypting stored frames.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference beyond the paper: the same selective-coordinate trick should transfer to compressed or mesh-based volumetric formats, since coordinates are stored as attributes there too; the paper's PLY-specific prototype limits the immediate claim, not the mechanism.
  • The paper's own 2000 MB cache anomaly suggests that once server and cache encryption cost is removed, disk I/O at the cache becomes the next bottleneck; this implies ABE-VVS shifts the performance frontier from crypto to storage, which segment-based streaming or faster caches would address.
  • Testable extension: the X-only security margin could be probed with a learned surface-completion model that predicts missing X from Y, Z, normals, and color; if such a model reconstructs recognizable geometry, ABE-X would still protect against casual viewers but not determined ones—an important deployment caveat the paper does not quantify.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 3 minor

Summary. The paper proposes ABE-VVS, a selective coordinate encryption framework for point-cloud volumetric video streaming. Instead of encrypting an entire PLY frame, the scheme uses CP-ABE to encrypt chosen subsets of the X, Y, Z coordinates while leaving all other attributes (color, normals, header) in clear. The authors measure encryption/decryption runtime across five point clouds and compare HTTP, HTTPS, and three ABE granularities (ABE-XYZ, ABE-XY, ABE-X) in a CloudLab streaming testbed, reporting server/cache CPU load, cache hit rate, rebuffering, and cache response time. The central claims are that ABE-X provides effective visual obfuscation while reducing encryption/decryption time by up to 50%/80%, and that the ABE streaming pipeline reduces server and cache CPU load relative to HTTPS while maintaining acceptable QoE.

Significance. If the security claim were sound, ABE-VVS would be a useful lightweight DRM layer for point-cloud volumetric video, and the end-to-end streaming evaluation would be a valuable systems contribution. The runtime measurements are clearly described, and the 1000-run encryption/decryption experiments give solid support for the performance trends. The streaming testbed, cache-aware comparison, and point-to-multipoint ABE rationale are also strengths. However, the central security property — that X-only encryption prevents meaningful unauthorized viewing — is not established and is, in fact, contradicted by the scheme's own design, because per-point surface normals are transmitted in the clear. The DRM contribution therefore rests on an unsupported and likely false claim.

major comments (3)
  1. [Sec. 4.1.4 and Algorithm 1, line 14] The security evaluation measures distortion only on a 'zeroed-coordinate' variant, but this is not a worst-case proxy for an unauthorized viewer. Algorithm 1 leaves normals, colors, Y, and Z in the clear. For a surface locally written as x(y,z), the normal (nx,ny,nz) gives dx/dy = -ny/nx and dx/dz = -nz/nx, so the missing X coordinate can be recovered up to an additive constant per connected surface by integrating these gradients. The zero-filling attacker discards this information; a reconstruction-aware attacker does not. The manuscript even acknowledges interpolation for coarser granularities but never evaluates normal-guided reconstruction for X-only encryption. The claim that ABE-X 'prevents meaningful unauthorized viewing' is therefore unsupported and contradicted by the information present in the encrypted stream. The authors should either encrypt normals, provide and evaluate a r
  2. [Abstract and Sec. 4.3.3, Fig. 9 (middle)] The abstract states that 'ABE-X achieves zero rebuffering comparable to HTTP-only.' This is true only in the 0 MB cache configuration. In the 2000 MB cache configuration, the paper reports rebuffering of 220–240% for all ABE schemes and HTTP-only, and about 275% for HTTPS. The authors attribute this to slow disk reads in the cache, but the headline claim is overbroad as written. The paper should explicitly qualify the cache configuration and discuss the implication for the claimed QoE benefit.
  3. [Sec. 4.2 and Sec. 4.3] The streaming results are based on a single measured run per scheme and cache configuration (one warm-up run plus one measured run). No confidence intervals, variance measures, or repeated trials are reported. Since the paper's quantitative claims include 'up to 80%' server CPU reduction and 'up to 63%' cache CPU reduction, the lack of statistical support weakens the precision of these claims. Adding multiple trials and reporting distributions would be necessary to substantiate the magnitude of the reported differences.
minor comments (3)
  1. [Sec. 4.1.4] The phrase 'theXYZ scheme' should read 'the XYZ scheme' (spacing). Also, Figure 6 captions inconsistently use 'Every X and Y zeroed' versus 'Every 2nd X zeroed'; consider normalizing capitalization and wording.
  2. [Sec. 4.2.2 / Fig. 9] The label 'Cache hitrates' should be 'Cache hit rates.' Additionally, the manuscript should define whether the reported rebuffering percentages are averages across all clients and clarify that values above 100% are possible because total stall time can exceed the nominal video duration.
  3. [Sec. 4.1.3] The paper states that encryption and decryption times 'scale nearly linearly' with point-cloud size but does not provide fitted slopes or correlation coefficients. A brief linear-fit summary would make the claim more precise.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the runtime/streaming results are empirical and independent, and the obfuscation-evaluation proxy is a security-correctness concern, not a circular derivation.

full rationale

The paper's central claims are empirical: encryption/decryption runtimes, CPU load, cache hit rates, and rebuffering are measured on CloudLab and reported directly (Secs. 4.1.3, 4.2, 4.3). No parameter is fitted and then renamed as a prediction, and no equation in Algorithm 1 or 2 is equivalent by construction to the claimed conclusion. Same-author citations [40,41,42,43] are used for provenance and comparison; Algorithm 1 restates the selective-encryption mechanism in full rather than importing it solely by citation, so those self-citations are not load-bearing for the new end-to-end evaluation. The one extrapolation supported by a same-group citation, [30], concerns linear ABE overhead with attribute count and is minor. The security/obfuscation discussion in Sec. 4.1.4 measures a zeroed-coordinate variant and asserts this is a worst-case proxy; that is a measurement choice, not a circular step. However, it is a genuine correctness/security gap: Algorithm 1 line 14 leaves normals and colors in clear, and the paper itself notes at the end of Sec. 4.1.4 that simple interpolation 'could partially reconstruct the missing geometry,' a threat it quantifies only for 2X-5X, not for X-only. That gap weakens the 'prevents meaningful unauthorized viewing' claim but does not make the derivation circular. Under the stated rules, the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The performance and security claims rest on the correctness of the CP-ABE toolkit, on treating zero-filled coordinates as the adversarial worst case, on interpreting Chamfer/Hausdorff distance as obfuscation strength, and on using one repeated point cloud as a representative video. The experimental configuration (buffer size, Poisson start schedule, single-attribute ABE policy, pattern P=X) is chosen by hand and shapes the headline numbers, but no numeric fitting is performed.

free parameters (4)
  • Client playback buffer = 6 seconds
    Rebuffering comparisons depend on buffer size; ABE-X's zero rebuffering is relative to this buffer and the Poisson start schedule (Sec 4.2.1).
  • Poisson start λ = 5
    Client concurrency schedule affects CPU load and rebuffering; chosen by the authors (Sec 4.2.1).
  • ABE attribute count = 1
    All crypto timings use a single-attribute policy; streaming deployments with more attributes are extrapolated via [30], not measured (Sec 4.1.3).
  • Encryption granularity pattern P = X (also XY, XYZ, 2X-5X evaluated)
    The headline result is for X-only; the choice is justified post-hoc by CD/HD metrics on the zeroed variant (Sec 3.2, Sec 4.1.4).
assumptions (5)
  • standard math CP-ABE toolkit correctly implements Bethencourt-Sahai-Waters ABE and provides the standard confidentiality/correctness guarantees.
    Relied on throughout Sec 3.3.1 and all experiments; not re-verified in this paper.
  • ad hoc to paper Zero-filled coordinates are a valid worst-case proxy for what an unauthorized viewer can reconstruct from an encrypted frame.
    Sec 4.1.4 defines obfuscation metrics on a zeroed variant, not on actual ABE-encrypted frames; this is an adversary assumption, not an established fact.
  • domain assumption Higher Chamfer/Hausdorff distance between original and zeroed cloud implies stronger visual obfuscation and less meaningful viewing.
    Sec 4.1.2 inverts the usual interpretation of these metrics; no user study or perceptual validation is provided.
  • domain assumption A 108k-point cloud looped to 1440 frames at 24 FPS is a representative volumetric video for evaluating streaming QoE.
    Sec 4.2.1 uses a single repeated point cloud; no motion or scene diversity.
  • domain assumption Single-attribute ABE policy timing extrapolates to deployed policies via linear scaling in number of attributes.
    Sec 4.1.3 invokes [30] to justify one-attribute experiments; no multi-attribute measurements are made in this paper.

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Cite this review

Pith. "Pith review of ABE-VVS: Attribute-Based Encrypted Volumetric Video Streaming." pith.science (2026). https://pith.science/paper/L242ISDX

@misc{pith2026260108987,
  author       = {Pith},
  title        = {Pith review of: ABE-VVS: Attribute-Based Encrypted Volumetric Video Streaming},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L242ISDX}},
  note         = {Machine review of arXiv:2601.08987}
}
abstract

This work introduces ABE-VVS, a framework that performs attribute based selective coordinate encryption for point cloud based volumetric video streaming, enabling lightweight yet effective digital rights management (DRM). Rather than encrypting entire point cloud frames, our approach encrypts only selected subsets of coordinates ($X, Y, Z$, or combinations), lowering computational overhead and latency while still producing strong visual distortion that prevents meaningful unauthorized viewing. Our experiments show that encrypting only the $X$ coordinates achieves effective obfuscation while reducing encryption and decryption times by up to 50% and 80%, respectively, compared to full-frame encryption. To our knowledge, this is the first work to provide a novel end-to-end evaluation of a DRM-enabled secure point cloud streaming system. We deployed a point cloud video streaming setup on the CloudLab testbed and evaluated three HTTP-based Attribute-Based Encryption (ABE) granularities - ABE-XYZ (encrypting all $X,Y,Z$ coordinates), ABE-XY, and ABE-X against conventional HTTPS/TLS secure streaming as well as an HTTP-only baseline without any security. Our streaming evaluation demonstrates that ABE-based schemes reduce server-side CPU load by up to 80% and cache CPU load by up to 63%, comparable to HTTP-only, while maintaining similar cache hit rates. Moreover, ABE-XYZ and ABE-XY exhibit lower client-side rebuffering than HTTPS, and ABE-X achieves zero rebuffering comparable to HTTP-only. Although ABE-VVS increases client-side CPU usage, the overhead is not large enough to affect streaming quality and is offset by its broader benefits, including simplified key revocation, elimination of per-client encryption, and reduced server and cache load.

Figures

Figures reproduced from arXiv: 2601.08987 by the authors.

Figure 2
Figure 2. illustrates the overall workflow of our selective-coordinate encryption pipeline. Our goal is to reduce computational cost by encrypting only a subset of point cloud coordinates. Rather than aiming for complete privacy, we target substantial visual obfuscation and distortion that prevents meaningful unauthorized viewing. To support flexible, per￾coordinate encryption, we extend the CPABE [4] toolkit with the ability… view at source ↗
Figure 1
Figure 1. System Architecture Our approach introduces a selective encryption mechanism tai￾lored to volumetric content (Sect. 3.2). Unlike conventional HTTP￾based delivery, where content is either fully encrypted (HTTPS) or entirely unencrypted (HTTP), we encrypt only targeted coordinates within each point cloud frame. To signal these encrypted compo￾nents, we extend the MPD with an “encryption level" attribute that specifies… view at source ↗
Figure 3
Figure 3. Visuals of four point clouds: 108k, 334k, 433k, 515k. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Evaluation of encryption times across various granularities and point cloud sizes. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Evaluation of decryption times across various granularities and point cloud sizes. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Visualization of point clouds with zeroed coordi [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Quality evaluation metrics: higher is better. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Average CPU usage; server (left), cache (middle) and per client (right). [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: Cache hitrates (left), average rebuffering per client (middle) and average cache response time per request (right). [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]

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Reference graph

Works this paper leans on

49 extracted references · 2 canonical work pages

  1. [1]

    Annaby, M.E

    M.H. Annaby, M.E. Mahmoud, H.A. Abdusalam, H.A. Ayad, and M.A. Rushdi

  2. [2]

    2024.Apache HTTP Server Project

    Apache. 2024.Apache HTTP Server Project. Retrieved 2024 from https://httpd. apache.org/

    2024

  3. [3]

    2024.Apache Traffic Server

    Apache. 2024.Apache Traffic Server. Retrieved 2024 from https://trafficserver. apache.org/

    2024

  4. [4]

    2011.Using the cpabe Toolkit

    John Bethencourt. 2011.Using the cpabe Toolkit. Retrieved 2024 from https: //acsc.cs.utexas.edu/cpabe/tutorial.html

    2011

  5. [5]

    John Bethencourt, Amit Sahai, and Brent Waters. 2007. Ciphertext-Policy Attribute-Based Encryption. In2007 IEEE Symposium on Security and Privacy (SP ’07). 321–334. doi:10.1109/SP.2007.11

    work page doi:10.1109/sp.2007.11 2007

  6. [6]

    Yumeka Chujo, Yusuke Tagashira, Yukiko Harada, Kenji Kanai, and Jiro Katto

  7. [7]

    Matthias De Fré, Jeroen van der Hooft, Tim Wauters, and Filip De Turck. 2024. Scalable MDC-Based Volumetric Video Delivery for Real-Time One-to-Many WebRTC Conferencing. InProceedings of the 15th ACM Multimedia Systems Conference(Bari, Italy)(MMSys ’24). Association for Computing Machinery, New York, NY, USA, 121–131. doi:10.1145/3625468.3647617

    arXiv 2024

  8. [8]

    In2024 International Symposium on Multimedia (ISM)

    Perceptual Quality Driven Point Cloud Compression for 6DoF 3D Point Cloud Streaming. In2024 International Symposium on Multimedia (ISM). 154–157. doi:10.1109/ISM63611.2024.00034

    arXiv 2024

Show all 49 references
  1. [9]

    Dmitry Duplyakin, Robert Ricci, Aleksander Maricq, Gary Wong, Jonathon Duerig, Eric Eide, Leigh Stoller, Mike Hibler, David Johnson, Kirk Webb, Aditya Akella, Kuangching Wang, Glenn Ricart, Larry Landweber, Chip Elliott, Michael Zink, Emmanuel Cecchet, Snigdhaswin Kar, and Pra...

    2019

  2. [10]

    2024.pidstat - Report statistics for Linux tasks.https://linux.die.net/man/ 1/pidstat

    die.net. 2024.pidstat - Report statistics for Linux tasks.https://linux.die.net/man/ 1/pidstat

    2024

  3. [11]

    Simon N. B. Gunkel, Rick Hindriks, Karim M. El Assal, Hans M. Stokking, Sylvie Dijkstra-Soudarissanane, Frank ter Haar, and Omar Niamut. 2021. VRComm: an end-to-end web system for real-time photorealistic social VR communication. In Proceedings of the 12th ACM Multimedia Syste...

    2021

  4. [12]

    Serhan Gül, Dimitri Podborski, Thomas Buchholz, Thomas Schierl, and Cornelius Hellge. 2020. Low-latency cloud-based volumetric video streaming using head motion prediction. InProceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Vi...

    2020

  5. [13]

    Mohammad Hosseini and Christian Timmerer. 2018. Dynamic Adaptive Point Cloud Streaming. InProceedings of the 23rd Packet Video Workshop (MMSys ’18). ACM, 25–30. doi:10.1145/3210424.3210429

    2018

  6. [14]

    Hadi Heidarirad and Mea Wang. 2025. VV-DASH: A Framework for Volumetric Video DASH Streaming. InProceedings of the 16th ACM Multimedia Systems Conference(Stellenbosch, South Africa)(MMSys ’25). Association for Computing Machinery, New York, NY, USA, 256–262. doi:10.1145/371267...

    2025

  7. [15]

    Tzu-Kuan Hung, I-Chun Huang, Samuel Rhys Cox, Wei Tsang Ooi, and Cheng- Hsin Hsu. 2022. Error Concealment of Dynamic 3D Point Cloud Streaming. InProceedings of the 30th ACM International Conference on Multimedia(Lisboa, Portugal)(MM ’22). Association for Computing Machinery, N...

    2022

  8. [16]

    Gangyang Hou, Bo Ou, Fei Peng, and Min Long. 2025. A Traitor Tracing and Access Control Method for Encrypted 3D Models Based on CP-ABE and Fair Watermark.IEEE Signal Processing Letters32 (2025), 696–700. doi:10.1109/LSP. 2024.3522860

    2025

  9. [17]

    Xin Jin, Zhaoxing Wu, Chenggen Song, Chunwei Zhang, and Xiaodong Li. 2016. 3D Point Cloud Encryption Through Chaotic Mapping, Vol. 9916. 119–129. doi:10. 1007/978-3-319-48890-5_12

    2016

  10. [18]

    Jong-Beom Jeong, Soonbin Lee, Dongmin Jang, and Eun-Seok Ryu. 2019. Towards 3DoF+ 360 Video Streaming System for Immersive Media.IEEE Access7 (2019), 136399–136408. doi:10.1109/ACCESS.2019.2942771

    2019

  11. [19]

    Alireza Jolfaei, Xin-Wen Wu, and Vallipuram Muthukkumarasamy. 2015. A 3D Object Encryption Scheme Which Maintains Dimensional and Spatial Stability. IEEE Transactions on Information Forensics and Security10, 2 (2015), 409–422. doi:10.1109/TIFS.2014.2378146

    2015

  12. [20]

    Yili Jin, Kaiyuan Hu, Junhua Liu, Fangxin Wang, and Xue Liu. 2024. From Capture to Display: A Survey on Volumetric Video. arXiv:2309.05658 [cs.MM] https://arxiv.org/abs/2309.05658

    2024 arXiv

  13. [21]

    Jianqiang Liu, Jian Yao, Jingmin Tu, and Junhao Cheng. 2019. Data-Adaptive Packing Method for Compression of Dynamic Point Cloud Sequences. In2019 IEEE International Conference on Multimedia and Expo (ICME). 904–909. doi:10. 1109/ICME.2019.00160

    2019

  14. [22]

    Xusheng Li, Jinqing Li, Xiaoqiang Di, Hongmei Guan, Mingao Zhang, Mengli Gao, and Makram Ibrahim. 2024. 3D point cloud encryption algorithm based on hybrid key and spatial maintenance.Journal of Information Security and Applications87 (2024), 103896. doi:10.1016/j.jisa.2024.103896

    2024

  15. [23]

    Zhaoyang Lyu, Zhifeng Kong, Xudong Xu, Liang Pan, and Dahua Lin. 2021. A Con- ditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion. CoRRabs/2112.03530 (2021). arXiv:2112.03530 https://arxiv.org/abs/2112.03530

    2021 arXiv

  16. [24]

    Junhua Liu, Boxiang Zhu, Fangxin Wang, Yili Jin, Wenyi Zhang, Zihan Xu, and Shuguang Cui. 2023. CaV3: Cache-assisted Viewport Adaptive Volumetric Video Streaming. In2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR). 173–183. doi:10.1109/VR55154.2023.00033

    2023

  17. [25]

    Claudiu Olteanu, Mihai Bucicoiu, and Marius Popa. 2016. SStream: An Infras- tructure for Streaming Multimedia Content Efficiently and Securely in a Hetero- geneous Environment. InExploring Services Science, Theodor Borangiu, Mon- ica Dragoicea, and Henriqueta Nóvoa (Eds.). Spr...

    2016

  18. [26]

    2024.numpy.random.poisson

    NumPy. 2024.numpy.random.poisson. Retrieved 2024 from https://numpy.org/ doc/stable/reference/random/generated/numpy.random.poisson.html

    2024

  19. [27]

    Jounsup Park, Mingyuan Wu, Kuan-Ying Lee, Bo Chen, Klara Nahrstedt, Michael Zink, and Ramesh Sitaraman. 2020. SEAWARE: Semantic Aware View Prediction System for 360-degree Video Streaming. In2020 IEEE International Symposium on Multimedia (ISM). 57–64. doi:10.1109/ISM.2020.00016

    2020

  20. [28]

    2025.Dataset

    open3d.org. 2025.Dataset. https://www.open3d.org/docs/release/tutorial/data/ index.html

    2025

  21. [29]

    Alex Feltus, and Susmit Shannigrahi

    David Reddick, F. Alex Feltus, and Susmit Shannigrahi. 2022. Case Study of At- tribute Based Access Control for Genomics Data Using Named Data Networking. InIEEE CCNC

    2022

  22. [30]

    Cédric Portaneri, Pierre Alliez, Michael Hemmer, Lukas Birklein, and Elmar Schoemer. 2019. Cost-driven framework for progressive compression of textured meshes. InProceedings of the 10th ACM Multimedia Systems Conference(Amherst, Massachusetts)(MMSys ’19). Association for Comp...

    2019

  23. [31]

    David Reddick, Justin Presley, Frank Alex Feltus, and Susmit Shannigrahi. 2022. WiP: AABAC-Automated Attribute Based Access Control for Genomics Data. InProceedings of the 27th ACM on Symposium on Access Control Models and Technologies. 217–222

    2022

  24. [32]

    Alex Feltus, and Susmit Shannigrahi

    David Reddick, Justin Presley, F. Alex Feltus, and Susmit Shannigrahi. 2022. AABAC – Automated Attribute Based Access Control for Genomics Data. arXiv:2204.04591 [cs.CR] https://arxiv.org/abs/2204.04591

    2022 arXiv

  25. [33]

    Eric Rescorla. 2018. The Transport Layer Security (TLS) Protocol Version 1.3. RFC 8446. doi:10.17487/RFC8446

    2018 doi

  26. [34]

    2021.Design of an NDN-Based Data Distribution Infras- tructure for Genomics Data Secured with a Novel Ledger Access System Founded on Attribute-Based Encryption

    David Eugene Reddick. 2021.Design of an NDN-Based Data Distribution Infras- tructure for Genomics Data Secured with a Novel Ledger Access System Founded on Attribute-Based Encryption. Master’s thesis. Tennessee Technological University

    2021

  27. [35]

    Jan-Philipp Stauffert, Florian Niebling, and Marc Erich Latoschik. 2020. Latency and Cybersickness: Impact, Causes, and Measures. A Review.Frontiers in Virtual RealityVolume 1 - 2020 (2020). doi:10.3389/frvir.2020.582204

    2020

  28. [36]

    Iraj Sodagar. 2011. The mpeg-dash standard for multimedia streaming over the internet.IEEE multimedia18, 4 (2011), 62–67

    2011

  29. [37]

    Zhongze Tang, Xianglong Feng, Yi Xie, Huy Phan, Tian Guo, Bo Yuan, and Sheng Wei. 2020. VVSec: Securing Volumetric Video Streaming via Benign Use of Ad- versarial Perturbation. InProceedings of the 28th ACM International Conference on Multimedia(Seattle, WA, USA)(MM ’20). Asso...

    2020

  30. [38]

    Junshu Tang, Zhijun Gong, Ran Yi, Yuan Xie, and Lizhuang Ma. 2022. LAKe-Net: Topology-Aware Point Cloud Completion by Localizing Aligned Keypoints. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1716–1725. doi:10.1109/CVPR52688.2022.00177

    2022

  31. [39]

    2023.CDN Basic – How CDNs handle encrypted content

    USAVPS. 2023.CDN Basic – How CDNs handle encrypted content. Retrieved 2024 from https://usavps.com/blog/17594/

    2023

  32. [40]

    2025.The PLY Polygon File Format

    Greg Turk. 2025.The PLY Polygon File Format. https://web.archive.org/web/ 20161204152348/http://www.dcs.ed.ac.uk/teaching/cs4/www/graphics/Web/ ply.html

    2025

  33. [41]

    Mohammad Waquas Usmani, Susmit Shannigrahi, and Michael Zink. 2025. Se- cure the Stream, Not the Hosts: Attribute-Based Encryption for DRM Enabled Video Streaming. InProceedings of the 16th ACM Multimedia Systems Conference (Stellenbosch, South Africa)(MMSys ’25). Association ...

    2025

  34. [42]

    Mohammad Waquas Usmani, Susmit Shannigrahi, and Michael Zink. 2025. Lightweight DRM for Volumetric Point Clouds through Attribute-Based Se- lective Coordinate Encryption. InProceedings of the Twenty-Sixth International Symposium on Theory, Algorithmic Foundations, and Protocol...

    2025

  35. [43]

    Mohammad Waquas Usmani, Sankalpa Timilsina, Michael Zink, and Susmit Shannigrahi. 2025. Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications. arXiv:2512.15823 [cs.CR] https://arxiv.org/abs/2512.15823

    2025

  36. [44]

    Mohammad Waquas Usmani, Susmit Shannigrahi, and Michael Zink. 2025. Secur- ing Immersive 360 Video Streams through Attribute-Based Selective Encryption. arXiv:2505.04466 [cs.MM] https://arxiv.org/abs/2505.04466

    2025 arXiv

  37. [45]

    Jeroen van der Hooft, Tim Wauters, Filip De Turck, Christian Timmerer, and Hermann Hellwagner. 2019. Towards 6DoF HTTP Adaptive Streaming Through Point Cloud Compression. InProceedings of the 27th ACM International Conference on Multimedia(Nice, France)(MM ’19). Association fo...

    2019

  38. [46]

    Begen, Filip De Turck, and Raimund Schatz

    Jeroen van der Hooft, Maria Torres Vega, Christian Timmerer, Ali C. Begen, Filip De Turck, and Raimund Schatz. 2020. Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming. In2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)....

    2020

  39. [47]

    Anlan Zhang, Chendong Wang, Bo Han, and Feng Qian. 2022. YuZu: Neural- Enhanced Volumetric Video Streaming. In19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22). USENIX Association, Renton, WA, 137–154. https://www.usenix.org/conference/nsdi22/prese...

    2022

  40. [48]

    Rui Xin, Shihan Lin, and Xiaowei Yang. 2023. Quantifying User Password Expo- sure to Third-Party CDNs. InPassive and Active Measurement: 24th International Conference, PAM 2023, Virtual Event, March 21–23, 2023, Proceedings. Springer- Verlag, Berlin, Heidelberg, 652–668. doi:1...

    2023 doi

  41. [2024]

    doi:10.1016/j.ijleo.2024.171680

    On 3D encryption schemes based on chaotic permutations and rotations with geometric stability.Optik300 (2024), 171680. doi:10.1016/j.ijleo.2024.171680

    2024

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.