Exploring Device-Oriented Video Encryption for Hierarchical Privacy Protection in AR Content Sharing

Aaron Quigley; Dongsheng Zheng; Junyan Zhang; Kexin Nie; Wen Hu; Yongquan Hu

REVIEW 4 major objections 4 minor 12 references

Exploring Device-Oriented Video Encryption for Hierarchical Privacy Protection in AR Content Sharing

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper argues that bitstream-level ROI video encryption can protect the physical background in AR content sharing far more efficiently than pixel-level encryption, with encryption intensity tuned to the exposing display device.

desk verdict A plausible design sketch that overstays its abstract: the device-to-privacy mapping is new but manual, and the efficiency numbers are inherited, so the current paper does not substantiate its headline claim. read the letter →

arxiv 2411.10964 v2 pith:JJ5LS22L submitted 2024-11-17 cs.HC

Yongquan Hu , Dongsheng Zheng , Kexin Nie , Junyan Zhang , Wen Hu , Aaron Quigley This is my paper · ORCID

classification cs.HC

keywords augmentedrealityvisualprivacyregionofinterestencryptionbitstream-levelhierarchicalHEVCselectiveARcontentsharing

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

This paper proposes that private details in the physical background of AR content sharing—faces, ID cards, display content—can be protected by encrypting only the regions of interest inside the compressed video stream, rather than the whole frame at the pixel level. It argues this bitstream-level approach is dramatically more efficient, citing a comparison where the same visual distortion (PSNR around 15 dB) costs 500 MB with pixel-level encryption versus 200 KB with the ROSS bitstream system. The encryption strength is then adjusted according to which AR display is receiving the content: projection is treated as the most exposed, smartphones as intermediate, and AR glasses as the most private. If correct, this gives AR collaboration a way to balance privacy protection against the real-time performance demands of augmented reality.

What carries the argument

The load-bearing mechanism is the Region Of Semantic Saliency (ROSS) encryption system, an ROI video encryption variant that algorithmically encrypts foreground objects at the bitstream level instead of requiring user selection. It works through the tile mechanism of the HEVC encoder, which codes distinct video areas independently so that only tiles covering sensitive objects are encrypted. The paper couples this with a semantic-importance classification—faces are highly important, displayed content moderately important, ID cards slightly important—and a device-safety ordering, so the encryption level is chosen per display device. This combination is what lets the system claim to conserve code stream and reduce encryption overhead while still protecting the physical background.

What would settle it

A controlled measurement showing that AR-glasses wearers' content is captured by nearby bystanders at rates equal to or higher than projector viewers would falsify the device-safety ordering, as would a benchmark where bitstream-level ROI encryption on real AR backgrounds yields significantly worse than the claimed 15 dB PSNR at 200 KB.

Watch

Extended reading notes

Core claim

The paper's central claim is that ROI video encryption, performed at the bitstream level during HEVC encoding, can be brought into AR content sharing to hide sensitive parts of the physical environment, and that the encryption intensity should scale with the privacy exposure of the target display. The authors treat projection as a public display with the lowest privacy safety level, smartphones as offering balanced control, and AR glasses as the most private because content is seen by a single wearer; more exposed devices receive stronger encryption. They report that at comparable compression quality (PSNR $\approx 15$ dB), pixel-level encryption consumes 500 MB while the bitstream-level ROSS system requires only 200 KB, and they position this as making hierarchical, device-aware privacy protection feasible without breaking real-time AR performance. The paper is explicit that the device risk ordering is a speculative example rather than a thorough risk assessment, and that the current implementation still selects the AR display manually.

Load-bearing premise

The entire encryption-strength schedule rests on the unmeasured assumption that projectors are the most exposed displays, smartphones are intermediate, and AR glasses are the safest.

Editorial extensions

If this is right

Physical backgrounds in AR sharing can be protected without encrypting the whole frame, so common sensitive items such as faces and cards can be hidden at a tiny fraction of the data cost of pixel-level encryption.
Privacy protection can be made display-aware: a projector feed can be encrypted more heavily than a smartphone or AR-glasses feed, so each viewer gets the minimum protection their exposure requires.
Because encryption happens inside the HEVC encoding stage, the approach can preserve real-time AR performance better than post-hoc pixel encryption.
The semantic-importance ranking provides a natural way to allocate encryption effort: the most sensitive object types receive the strongest protection first.

Reading between the lines

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

The 500 MB versus 200 KB figure comes from the underlying ROSS system on a standard test sequence, not from the AR hierarchy itself; an AR-specific benchmark would be needed to confirm the savings survive real background scenes and multiple tile partitions.
The assumed device ordering could be tested directly: measuring actual shoulder-surfing or bystander capture rates for projection, smartphone, and AR glasses in controlled settings would tell whether projector really is the riskiest.
A natural extension is to automate device detection and encryption-level switching using existing sensor or hardware identification methods, turning the manual selection into the end-to-end system the authors plan.
The semantic-importance categories are limited to three example objects; a larger taxonomy, such as license plates, medical records, or screens, would be needed before deployment.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: the kernel idea—basing ROI encryption strength on which AR display receives the shared video—is a reasonable, under-explored direction. The authors are also upfront that their device-privacy ordering is speculation, not a risk assessment. But the paper does not deliver what the abstract promises. The abstract says the system is adaptive and dynamically adjusts encryption intensity, while Section 2.2 says device selection and encryption level are chosen manually before each encryption. That is not a missing experiment; it is a mismatch between the central claim and the described implementation. The Limitations section lists automation as future work, which compounds the problem.

What is genuinely new: the three-tier mapping from display type (projector, smartphone, AR glasses) to a privacy safety level, and the suggestion to vary semantic-object encryption accordingly. Nothing in the cited literature does exactly that. The paper also positions itself competently within AR privacy and ROI encryption work, and it explicitly flags the risk ordering as a guess. Those are good-faith moves.

The soft spots are large. The 500MB-to-200KB efficiency figure comes entirely from the authors' prior ROSS paper, not from any experiment here, so it adds no new evidence. The privacy-safety ordering is asserted without user study or contextual analysis, as the authors admit. And the adaptive claim is contradicted by the manual selection description. Read generously, this is a well-scoped suggestion for future work; read literally, it is an overclaim.

Who is this for? A workshop audience working on AR privacy would get a conversation starter. A reader looking for a validated technique will be disappointed. I would not cite it in its current form, and I would not send it to a serious referee until the abstract is aligned with the implemented system and at least one small demonstration is added—even a simulated encryption-level switch across the three device types would help. As is, it is a sketch, not a contribution.

Referee Report

4 major / 4 minor

Summary. The paper proposes a device-oriented hierarchical video encryption scheme for AR content sharing, in which the privacy safety of three display types (projector/SAR, smartphone/HHD, AR glasses/HMD) is used to determine the encryption strength applied to regions of interest such as faces, display content, and ID cards. The system builds on the authors' prior ROSS bitstream-level ROI encryption system and claims improved efficiency and adaptive behavior. The manuscript reports no new experiments: Section 3 imports the key efficiency numbers from prior work [7], Section 2.2 states that device selection is performed manually, and Section 4 defers automation, user studies, and performance tests to future work.

Significance. Privacy protection in multi-device AR content sharing is a timely and practically important problem. Combining bitstream-level ROI encryption with device-dependent privacy levels is a reasonable direction, and the paper clearly identifies the trade-off between encryption safety and real-time performance. If the claims were validated, the approach could reduce encrypted data volume compared with pixel-level encryption while adapting protection to display context. However, none of the novel components are validated in this manuscript: the device-to-safety mapping is admittedly speculative, the semantic importance ordering is asserted without rationale, and the quantitative efficiency evidence is lifted from the authors' own prior work. The paper's main strength is its honest articulation of limitations, not empirical support for its central claims.

major comments (4)

[Sec. 2.2 and Sec. 4] The abstract's central claim of an 'adaptive system [that] dynamically adjusts the encryption intensity based on the AR display method' is contradicted by the described implementation. Section 2.2 states that the device is 'manually determine[d]' and the encryption level is selected 'prior to each encryption,' and Section 4 lists a 'fully automated end-to-end system' as future work. The system as presented is therefore a manual per-device preset, not an adaptive or dynamic mechanism, so the paper's headline claim is not supported by its own methodology.
[Sec. 3] The only quantitative result supporting the efficiency claim, the comparison between pixel-level encryption (500 MB) and bitstream-level ROSS encryption (200 KB) at PSNR ≈ 15 dB, is taken from the authors' prior paper [7] and is not reproduced or extended to the AR content-sharing context. Since this number is the sole evidence for the claim of a 'more efficient solution,' the central efficiency advantage rests entirely on a self-citation rather than on any measurement, trace, or benchmark presented here.
[Sec. 3] The core input to the proposed system, the privacy safety ordering of display devices (projector lowest, smartphone balanced, AR glasses highest), is acknowledged in the text as 'a mere example and not a thorough risk assessment.' The paper also asserts without support or justification that ID cards are 'slightly important' while display content is 'moderately important,' which is counterintuitive given the sensitivity of ID cards in AR backgrounds. Because the hierarchical encryption levels are determined by these orderings, an incorrect ordering would mis-target the actual privacy threats; no threat model, user study, or domain analysis is provided to substantiate either ordering.
[Secs. 1 and 4] No experiments, user study, or performance test are reported, and Section 4 explicitly states that user studies and performance tests are future work. Consequently, the stated goal of balancing 'encryption safety' and 'real-time performance' is never evaluated, and there is no evidence that the proposed system is usable in a realistic AR pipeline. The manuscript is a position statement rather than a validated system paper, which leaves the central claims unsupported.

minor comments (4)

[Fig. 2 caption] The phrase 'title number: 12×16' appears to be a typo for 'tile number: 12×16'; please correct it and define the tile configuration in the text.
[Fig. 1 and Sec. 2] The acronyms SAR, HHD, and HMD are used in the figure caption but are not defined in the text where they first appear; please define them at first use.
[Sec. 2.1] Reference [7] is cited as the source for the ROSS system, but the reference title is 'SDM: Semantic Distortion Measurement for Video Encryption'; the relationship between ROSS and SDM should be stated explicitly so readers can locate the system description.
[Sec. 3] The mapping from device privacy safety level to encryption level is described only qualitatively; an explicit table or formula showing how the safety level determines concrete encryption parameters (e.g., QP, tile configuration, or encryption strength) would make the proposal easier to test and reproduce.

Circularity Check

1 steps flagged · score 6.0 of 10

Efficiency advantage is the authors' prior ROSS numbers imported as this paper's finding; no AR experiment is reported, and the 'adaptive' system is admitted to be manual preselection.

self citation load bearing [Abstract; Sec. 2.1; Sec. 3 (Findings & Discussions)]
"This study pioneers the integration of ROI video encryption at the bitstream level within AR contexts, providing a more efficient solution than traditional pixel-level encryption by enhancing encryption speed and reducing the required space. At a comparable encryption quality of PSNR ≈15dB, pixel-level encryption consumes 500MB, while the ROSS system at the bitstream level requires only 200KB. We utilize the Region Of Semantic Saliency (ROSS) encryption system, as detailed in [7]."

The quantitative efficiency claim that anchors the paper's central contribution is not derived or measured anywhere in this manuscript; it is the verbatim result of the authors' own prior ROSS paper [7], which shares the first author. No AR-specific test, reproduction of the 500MB/200KB figures, or comparison in the proposed projection/smartphone/AR-glasses pipeline is reported here. Thus the claimed 'more efficient than pixel-level encryption in AR contexts' reduces to the self-cited prior output rather than to an independent finding of this study. The paper's own Sec. 2.2 states that device selection and encryption-level choice are performed manually prior to encryption, and Sec.

full rationale

Score 6: the central efficiency claim is load-bearing and is supported only by the authors' prior ROSS paper [7]; the 500MB vs 200KB comparison is imported without reproduction, so the 'prediction' that bitstream-level ROI encryption is more efficient in AR contexts equals the cited prior result. The manuscript itself flags key limitations: Sec. 2.2 says 'Currently, we manually determine the appropriate AR device for display and select the corresponding encryption level prior to each encryption,' which contradicts the abstract's 'adaptive system dynamically adjusts'; Sec. 4 repeats this with 'we aim to develop a fully automated end-to-end system' and defers 'more comprehensive experiments' to future work; Sec. 3 calls the risk ordering 'a mere example and not a thorough risk assessment.' These admissions mean the device-oriented hierarchy is an asserted mapping, not a demonstrated result. I did not count the risk ordering itself as a separate circular step because the paper explicitly labels it a speculative example; the main circularity is the efficiency figure reducing to self-citation. Because ROSS is a published prior system, the paper is not a pure definitional tautology, but in this AR context the central quantitative claim is unvalidated and rests on the authors' own earlier work, giving 6 rather than 8.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper's contribution rests on the assumed validity of ROSS, a speculative device risk ordering, and an example-based semantic importance list. None of these is independently established in this paper.

free parameters (1)

Device-to-privacy-safety mapping = Qualitative ordering: projector < smartphone < AR glasses
The device-oriented encryption levels depend on an assumed, unquantified risk ordering stated as speculation in Sec 3.

assumptions (3)

domain assumption ROSS system encrypts foreground objects algorithmically and achieves the cited bitstream-level efficiency
The central method is taken from self-cited prior work [7] and not re-implemented here.
ad hoc to paper Display devices have inherent privacy risk ordering
Stated in Sec 3: "We speculate projectors, as public displays, pose the greatest privacy risk..." and "not a thorough risk assessment."
ad hoc to paper Object categories (face, display content, ID card) cover relevant sensitivity levels
Defined "as an example" in Sec 3, not validated empirically.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Exploring Device-Oriented Video Encryption for Hierarchical Privacy Protection in AR Content Sharing." pith.science (2026). https://pith.science/paper/JJ5LS22L

@misc{pith2026241110964,
  author       = {Pith},
  title        = {Pith review of: Exploring Device-Oriented Video Encryption for Hierarchical Privacy Protection in AR Content Sharing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JJ5LS22L}},
  note         = {Machine review of arXiv:2411.10964}
}

read the original abstract

Content sharing across multiple Augmented Reality (AR) displays is becoming commonplace, enhancing team communication and collaboration through devices like smartphones and AR glasses. However, this practice raises significant privacy concerns, especially concerning the physical environment visible in AR, which may include sensitive personal details like facial features and identifiable information. Our research focuses on protecting privacy within AR environments, particularly the physical backgrounds visible during content sharing across three common AR display methods: projection, smartphone, and AR glasses. We analyze the potential privacy risks associated with each method and employ a Region Of Interest (ROI) video encryption system to hierarchically encrypt the physical backdrop based on its safety rating. This study pioneers the integration of ROI video encryption at the bitstream level within AR contexts, providing a more efficient solution than traditional pixel-level encryption by enhancing encryption speed and reducing the required space. Our adaptive system dynamically adjusts the encryption intensity based on the AR display method, ensuring tailored privacy protection.

Figures

Figures reproduced from arXiv: 2411.10964 by the authors.

**Figure 2.** Tracking and encryption effect on Kimono1 standard test [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

12 extracted references · 11 canonical work pages

[7]

Y . Hu, W. Zhou, S. Zhao, Z. Chen, and W. Li. Sdm: Semantic dis- tortion measurement for video encryption. In 2018 13th IEEE Inter- national Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 764–768. IEEE, 2018. 2

work page 2018
[1]

Billinghurst and H

M. Billinghurst and H. Kato. Collaborative augmented reality. Com- munications of the ACM, 45(7):64–70, 2002. 1

work page 2002
[2]

Cowan, A

K. Cowan, A. Javornik, and P. Jiang. Privacy concerns when using augmented reality face filters? explaining why and when use avoid- ance occurs. Psychology & Marketing, 38(10):1799–1813, 2021. 1

work page 2021
[3]

R. Du, E. Lee, and A. Varshney. Tracking-tolerant visual cryptogra- phy. In 2019 IEEE Conference on Virtual Reality and 3D User Inter- faces (VR), pp. 902–903. IEEE, 2019. 1

work page 2019
[4]

Farajallah, W

M. Farajallah, W. Hamidouche, O. D ´eforges, and S. El Assad. Roi encryption for the hevc coded video contents. In 2015 IEEE Interna- tional Conference on Image Processing (ICIP), pp. 3096–3100. IEEE,

work page 2015
[5]

Hartmann, Y .-T

J. Hartmann, Y .-T. Yeh, and D. V ogel. Aar: Augmenting a wearable augmented reality display with an actuated head-mounted projector. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, pp. 445–458, 2020. 1

work page 2020
[6]

Y . Hu, M. Yuan, K. Xian, D. S. Elvitigala, and A. Quigley. Explor- ing the design space of employing ai-generated content for augmented reality display. arXiv preprint arXiv:2303.16593, 2023. 2

arXiv 2023
[8]

S. M. Lehman, A. S. Alrumayh, K. Kolhe, H. Ling, and C. C. Tan. Hidden in plain sight: Exploring privacy risks of mobile augmented reality applications. ACM Transactions on Privacy and Security , 25(4):1–35, 2022. 1

work page 2022

Show all 12 references

[9]

Massoudi, F

A. Massoudi, F. Lefebvre, C. De Vleeschouwer, B. Macq, and J.-J. Quisquater. Overview on selective encryption of image and video: challenges and perspectives. Eurasip Journal on information security, 2008(1):179290, 2008. 2

2008
[10]

Nuernberger, E

B. Nuernberger, E. Ofek, H. Benko, and A. D. Wilson. Snaptoreal- ity: Aligning augmented reality to the real world. In Proceedings of the 2016 CHI conference on human factors in computing systems , pp. 1233–1244, 2016. 1

2016
[11]

J. R. Padilla-L ´opez, A. A. Chaaraoui, and F. Fl ´orez-Revuelta. Visual privacy protection methods: A survey. Expert Systems with Applica- tions, 42(9):4177–4195, 2015. 1

2015
[12]

Y . Suh, Y . Park, H. Yoon, Y . Chang, and W. Woo. Context-aware mobile ar system for personalization, selective sharing, and interaction of contents in ubiquitous computing environments. In International Conference on Human-Computer Interaction , pp. 966–974. Springer,

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [7]

Y . Hu, W. Zhou, S. Zhao, Z. Chen, and W. Li. Sdm: Semantic dis- tortion measurement for video encryption. In 2018 13th IEEE Inter- national Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 764–768. IEEE, 2018. 2

work page 2018

[2] [1]

Billinghurst and H

M. Billinghurst and H. Kato. Collaborative augmented reality. Com- munications of the ACM, 45(7):64–70, 2002. 1

work page 2002

[3] [2]

Cowan, A

K. Cowan, A. Javornik, and P. Jiang. Privacy concerns when using augmented reality face filters? explaining why and when use avoid- ance occurs. Psychology & Marketing, 38(10):1799–1813, 2021. 1

work page 2021

[4] [3]

R. Du, E. Lee, and A. Varshney. Tracking-tolerant visual cryptogra- phy. In 2019 IEEE Conference on Virtual Reality and 3D User Inter- faces (VR), pp. 902–903. IEEE, 2019. 1

work page 2019

[5] [4]

Farajallah, W

M. Farajallah, W. Hamidouche, O. D ´eforges, and S. El Assad. Roi encryption for the hevc coded video contents. In 2015 IEEE Interna- tional Conference on Image Processing (ICIP), pp. 3096–3100. IEEE,

work page 2015

[6] [5]

Hartmann, Y .-T

J. Hartmann, Y .-T. Yeh, and D. V ogel. Aar: Augmenting a wearable augmented reality display with an actuated head-mounted projector. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, pp. 445–458, 2020. 1

work page 2020

[7] [6]

Y . Hu, M. Yuan, K. Xian, D. S. Elvitigala, and A. Quigley. Explor- ing the design space of employing ai-generated content for augmented reality display. arXiv preprint arXiv:2303.16593, 2023. 2

arXiv 2023

[8] [8]

S. M. Lehman, A. S. Alrumayh, K. Kolhe, H. Ling, and C. C. Tan. Hidden in plain sight: Exploring privacy risks of mobile augmented reality applications. ACM Transactions on Privacy and Security , 25(4):1–35, 2022. 1

work page 2022