pith. sign in

arxiv: 2606.08851 · v1 · pith:CCNXEBC6new · submitted 2026-06-07 · 💻 cs.SI · cs.CY

Enforcing Trust Accountability with Backward Propagation

Wenbo Wu , George Konstantinidis This is my paper

Pith reviewed 2026-06-27 17:19 UTC · model grok-4.3

classification 💻 cs.SI cs.CY
keywords trust propagationreputation systemsbackward propagationendorsement networksaccountabilitycold-startdistributed networksinteraction feedback
0
0 comments X

The pith

A two-layer model couples endorsements with interactions and uses backward propagation to enforce accountability for trust signals.

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

Existing trust models only push signals forward from past interactions, leaving endorsers unaccountable when those signals lead to harm and leaving new nodes without initial trust values. RepuLink adds an endorsement layer on top of the interaction layer and runs two concurrent backward mechanisms: one that recursively penalizes endorsers of misbehaving nodes and one that rewards endorsers of well-performing nodes. The result is an enforceable accountability loop plus an endorser-weighted way to initialize trust for newcomers. Experiments on real datasets show gains on four metrics against forward-only baselines while keeping similar running time.

Core claim

RepuLink is a two-layer reputation model that couples an endorsement network with an interaction feedback network. It integrates Backward Endorsement Penalty Propagation (BEPP), which recursively penalizes endorsers of misbehaving nodes, and Backward Endorsement Reward Propagation (BERP), which rewards endorsers of well-performing nodes. Together these mechanisms enforce endorsement accountability, create a positive interaction feedback loop, and supply explainable, endorser-weighted trust initialization for newly joined nodes.

What carries the argument

The two concurrent backward propagation mechanisms (BEPP for penalties and BERP for rewards) operating on the endorsement layer that is coupled to the interaction feedback layer.

If this is right

  • Endorsers become directly accountable for the future behavior of the nodes they endorse.
  • Well-performing nodes generate rewards that flow back to their endorsers, creating an incentive for careful endorsement.
  • New nodes receive an initial trust value derived from the weighted trust of their endorsers rather than starting from zero or uniform values.
  • The combined mechanisms form a closed positive feedback loop between endorsements and observed interactions.
  • The approach maintains comparable computational cost to forward-only models while improving four evaluation metrics on real interaction data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same backward accountability idea could be tested in content-sharing platforms where users endorse posts or accounts.
  • If endorsement data is sparse or noisy, the initialization benefit for new nodes would shrink, suggesting a need for hybrid initialization rules.
  • The model implicitly assumes that the endorsement graph itself does not contain coordinated bad actors; detecting such clusters would require additional machinery.
  • Dynamic networks with frequent node arrival and departure could be used to measure how quickly the backward signals stabilize the reputation scores.

Load-bearing premise

The model assumes that an endorsement network can be meaningfully coupled with the interaction feedback network such that backward propagation produces enforceable accountability without introducing new vulnerabilities or requiring unavailable data.

What would settle it

A dataset or live deployment in which endorsers of misbehaving nodes receive no effective penalty yet the model still claims superior metric scores, or in which the required endorsement data is missing for a large fraction of nodes.

Figures

Figures reproduced from arXiv: 2606.08851 by George Konstantinidis, Wenbo Wu.

Figure 1
Figure 1. Figure 1: Traditional Trust vs. Accountable Trust 1 Introduction Trust is the foundation of reliable interactions in distributed net￾works [40], and is crucial for encouraging active user engagement in applications such as data markets [10, 24, 28] and autonomous vehicles [14, 36]. Trust and reputation management systems [11, 34] have emerged as effective methods to quantify the trustworthiness of participants based… view at source ↗
Figure 2
Figure 2. Figure 2: RepuLink Model. B performed badly in Layer 2 and received negative feedback from D. A as the endorser of B will be [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Time-evolving reputation dynamics under RepuLink over [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Demonstration of initial reputation assignment for a new node under different endorsement scenarios. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Convergence Curve of RepuLink [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Parameter sensitivity on Bitcoin-OTC (top) and Bitcoin-Alpha (bottom). [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Rank Change Distribution 1k 2k 3k 4k 5k 6k Rank (Forward Only) 1k 2k 3k 4k 5k 6k Rank (With BEPP)Forward Only vs. With BEPP Bitcoin-OTC Bitcoin-Alpha 1k 2k 3k 4k 5k 6k Rank (Forward Only) 1k 2k 3k 4k 5k 6k Rank (With BERP)Forward Only vs. With BERP Bitcoin-OTC Bitcoin-Alpha 1k 2k 3k 4k 5k 6k Rank (Forward Only) 1k 2k 3k 4k 5k 6k Rank (Full Model)Forward Only vs. Full Model Bitcoin-OTC Bitcoin-Alpha 1k 2k 3… view at source ↗
Figure 8
Figure 8. Figure 8: Rank Comparison scatter plot comparing With BEPP vs. Full Model (4 𝑡ℎ of [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
read the original abstract

Trust and reputation management underpins reliable interactions in distributed networks, yet existing trust models rely solely on forward propagation of interaction-based trust signals. They lack robust mechanisms to enforce accountability for the propagated trust signals when negative interactions occur. In addition, such models often fail to initialize newly joined nodes with sparse interaction history, leading to the cold-start problem. In this paper, we propose RepuLink, a two-layer reputation model that couples an endorsement network with an interaction feedback network. RepuLink integrates two concurrent backward propagation mechanisms: Backward Endorsement Penalty Propagation (BEPP), which recursively penalizes endorsers of misbehaving nodes, and Backward Endorsement Reward Propagation (BERP), which rewards endorsers of well-performing nodes. Together, RepuLink enforces endorsement accountability and incentivizes positive behaviors, which form a positive interaction feedback loop. The endorsement layer further provides explainable, endorser-weighted trust initialization for newly joined nodes. Experiments on real-world datasets against representative trust propagation baselines demonstrate that RepuLink outperforms across four evaluation metrics in both interaction-only and full two-layer settings, while preserving comparable efficiency.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes RepuLink, a two-layer reputation model coupling an endorsement network with an interaction feedback network. It introduces concurrent backward propagation mechanisms—Backward Endorsement Penalty Propagation (BEPP) and Backward Endorsement Reward Propagation (BERP)—to enforce accountability for propagated trust signals and address the cold-start problem via endorser-weighted initialization. Experiments on real-world datasets are reported to show outperformance over trust propagation baselines across four metrics in both interaction-only and full two-layer settings, with comparable efficiency.

Significance. If the two-layer results hold and generalize, the work would be significant for trust and reputation systems in distributed networks by adding enforceable accountability through backward mechanisms and a positive feedback loop, while also providing an explainable initialization method. This extends forward-only propagation models in a way that could improve reliability in settings with misbehavior.

major comments (2)
  1. [Abstract] Abstract: the central claim of outperformance in the full two-layer setting (across four metrics) depends on the endorsement network being available and meaningfully coupled to the interaction logs in the real-world datasets without introducing unavailable data or new vulnerabilities. No information is provided on how this layer is obtained, constructed, or validated, which is load-bearing for attributing gains to BEPP/BERP rather than data artifacts.
  2. [Abstract] The assumption that an endorsement network can be coupled without new vulnerabilities is stated but not tested or analyzed for security implications in the two-layer experiments; this directly affects the enforceability claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each major comment point by point below, and will incorporate revisions where appropriate to strengthen the paper.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim of outperformance in the full two-layer setting (across four metrics) depends on the endorsement network being available and meaningfully coupled to the interaction logs in the real-world datasets without introducing unavailable data or new vulnerabilities. No information is provided on how this layer is obtained, constructed, or validated, which is load-bearing for attributing gains to BEPP/BERP rather than data artifacts.

    Authors: We agree that additional details on the endorsement network are necessary to support the claims. The full manuscript describes the real-world datasets in the experimental section, but we acknowledge the abstract does not. In the revision, we will modify the abstract to include a concise description of how the endorsement networks are sourced and coupled from the datasets (e.g., from existing social or trust links in the data). We will also add validation details to ensure the performance improvements are due to the BEPP/BERP mechanisms. revision: yes

  2. Referee: [Abstract] The assumption that an endorsement network can be coupled without new vulnerabilities is stated but not tested or analyzed for security implications in the two-layer experiments; this directly affects the enforceability claim.

    Authors: The paper assumes the endorsement network is provided as input, similar to how interaction networks are given in standard trust models. The enforceability claim pertains to the backward propagation enforcing accountability for endorsements. We have not conducted a dedicated security analysis of potential new vulnerabilities introduced by the coupling, as the primary contribution is the reputation model itself. We will revise to explicitly state this scope and suggest security analysis as future work, but maintain that the current experiments demonstrate the model's effectiveness under the stated assumptions. revision: partial

Circularity Check

0 steps flagged

No circularity; proposal is experimental and externally falsifiable

full rationale

The abstract and available text introduce RepuLink as a new two-layer model coupling endorsement and interaction networks via BEPP/BERP mechanisms, with claims of outperformance on real-world datasets across four metrics. No equations, parameter-fitting procedures, self-citations, or derivation steps are visible that reduce any result to its own inputs by construction. The central claims rest on empirical comparison to baselines, which is externally checkable and does not rely on self-definitional or fitted-input patterns. The paper is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; all details are deferred to the unavailable full text.

pith-pipeline@v0.9.1-grok · 5714 in / 1053 out tokens · 18752 ms · 2026-06-27T17:19:58.588372+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

48 extracted references · 33 canonical work pages

  1. [1]

    Sateesh Kumar Awasthi and Yatindra Nath Singh. 2020. Absolutetrust: algorithm for aggregation of trust in peer-to-peer networks.IEEE transactions on dependable and secure computing19, 1 (2020), 176–189. doi:10.1109/TDSC.2020.2977641

    work page doi:10.1109/tdsc.2020.2977641 2020

  2. [2]

    Michail Bampatsikos, Ilias Politis, Christos Xenakis, and Stelios CA Thomopoulos

  3. [3]

    InProceedings of the 16th International Conference on A vailability, Reliability and Security

    Solving the cold start problem in Trust Management in IoT. InProceedings of the 16th International Conference on A vailability, Reliability and Security. 1–9. doi:10.1145/3465481.3469208

    work page doi:10.1145/3465481.3469208

  4. [4]

    Arti Bandhana, Tomáš Kroupa, and Sebastián García. 2024. Trust in Shapley: A Cooperative Quest for Global Trust in P2P Network. InProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 132–140

    2024

  5. [5]

    Javier Castro, Daniel Gómez, and Juan Tejada. 2009. Polynomial calculation of the Shapley value based on sampling.Computers & operations research36, 5 (2009), 1726–1730

    2009

  6. [6]

    Hugh Chen, Ian C Covert, Scott M Lundberg, and Su-In Lee. 2023. Algorithms to estimate Shapley value feature attributions.Nature Machine Intelligence5, 6 (2023), 590–601. doi:10.1038/s42256-023-00657-x

    work page doi:10.1038/s42256-023-00657-x 2023

  7. [7]

    Xiang Cheng, Sen Su, Zhongbao Zhang, Hanchi Wang, Fangchun Yang, Yan Luo, and Jie Wang. 2011. Virtual network embedding through topology-aware node ranking.ACM SIGCOMM Computer Communication Review41, 2 (2011), 38–47. doi:10.1145/1971162.1971168

    work page doi:10.1145/1971162.1971168 2011

  8. [8]

    Xinxin Fan, Ling Liu, Mingchu Li, and Zhiyuan Su. 2016. GroupTrust: Dependable trust management.IEEE Transactions on Parallel and Distributed Systems28, 4 (2016), 1076–1090. doi:10.1109/TPDS.2016.2611660

    work page doi:10.1109/tpds.2016.2611660 2016

  9. [9]

    Xinxin Fan, Ling Liu, Rui Zhang, Quanliang Jing, and Jingping Bi. 2020. Decen- tralized trust management: Risk analysis and trust aggregation.ACM Computing Surveys (CSUR)53, 1 (2020), 1–33. doi:10.1145/3362168

    work page doi:10.1145/3362168 2020

  10. [10]

    Junmei Feng, Zhaoqiang Xia, Xiaoyi Feng, and Jinye Peng. 2021. RBPR: A hybrid model for the new user cold start problem in recommender systems.Knowledge- Based Systems214 (2021), 106732. doi:10.1016/j.knosys.2020.106732

    work page doi:10.1016/j.knosys.2020.106732 2021

  11. [11]

    Raul Castro Fernandez, Pranav Subramaniam, and Michael J Franklin. 2020. Data Market Platforms: Trading Data Assets to Solve Data Problems.Proceedings of the VLDB Endowment13, 11 (2020)

    2020

  12. [12]

    Jones Granatyr, Vanderson Botelho, Otto Robert Lessing, Edson Emílio Scalabrin, Jean-Paul Barthès, and Fabrício Enembreck. 2015. Trust and reputation models for multiagent systems.ACM Computing Surveys (CSUR)48, 2 (2015), 1–42. doi:10.1145/2816826

    work page doi:10.1145/2816826 2015

  13. [13]

    Taha Gunes, Long Tran-Thanh, and Timothy Norman. 2019. Identifying vul- nerabilities in trust and reputation systems. International Joint Conferences on Artificial Intelligence. doi:10.24963/ijcai.2019/44

    work page doi:10.24963/ijcai.2019/44 2019

  14. [14]

    Guibing Guo, Jie Zhang, and Daniel Thalmann. 2014. Merging trust in collabora- tive filtering to alleviate data sparsity and cold start.Knowledge-Based Systems 57 (2014), 57–68. doi:10.1016/j.knosys.2013.12.007

    work page doi:10.1016/j.knosys.2013.12.007 2014

  15. [15]

    Chao He, Tom H Luan, Rongxing Lu, Zhou Su, and Mianxiong Dong. 2022. Security and privacy in vehicular digital twin networks: Challenges and solutions. IEEE Wireless Communications30, 4 (2022), 154–160. doi:10.1109/MWC.002. 2200015

    work page doi:10.1109/mwc.002 2022

  16. [16]

    Ming Ji, Jiawei Han, and Marina Danilevsky. 2011. Ranking-based classification of heterogeneous information networks. InProceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. 1298–1306. doi:10.1145/2020408.2020603

    work page doi:10.1145/2020408.2020603 2011

  17. [17]

    Wenjun Jiang, Guojun Wang, Md Zakirul Alam Bhuiyan, and Jie Wu. 2016. Understanding graph-based trust evaluation in online social networks: Method- ologies and challenges.Acm Computing Surveys (Csur)49, 1 (2016), 1–35. doi:10.1145/2906151

    work page doi:10.1145/2906151 2016

  18. [18]

    Ruoming Jin, Victor E Lee, and Hui Hong. 2011. Axiomatic ranking of network role similarity. InProceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. 922–930. doi:10.1145/2020408.2020561

    work page doi:10.1145/2020408.2020561 2011

  19. [19]

    Audun Jøsang, Roslan Ismail, and Colin Boyd. 2007. A survey of trust and reputation systems for online service provision.Decision support systems43, 2 (2007), 618–644. doi:10.1016/j.dss.2005.05.019

    work page doi:10.1016/j.dss.2005.05.019 2007

  20. [20]

    Sepandar D Kamvar, Mario T Schlosser, and Hector Garcia-Molina. 2003. The eigentrust algorithm for reputation management in p2p networks. InProceedings of the 12th international conference on World Wide Web. 640–651. doi:10.1145/ 775152.775242

    arXiv 2003

  21. [21]

    Cristobald de Kerchove and Paul Van Dooren. 2008. The pagetrust algorithm: How to rank web pages when negative links are allowed?. InProceedings of the 2008 SIAM international conference on data mining. SIAM, 346–352. doi:10.1137/1. 9781611972788.31

    work page doi:10.1137/1 2008

  22. [22]

    Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, and VS Subrahmanian. 2018. Rev2: Fraudulent user prediction in rating platforms. In Proceedings of the eleventh ACM international conference on web search and data mining. 333–341

    2018

  23. [23]

    Srijan Kumar, Francesca Spezzano, VS Subrahmanian, and Christos Faloutsos

  24. [24]

    In2016 IEEE 16th international conference on data mining (ICDM)

    Edge weight prediction in weighted signed networks. In2016 IEEE 16th international conference on data mining (ICDM). IEEE, 221–230

  25. [25]

    Peter A Lofgren, Siddhartha Banerjee, Ashish Goel, and Comandur Seshadhri

  26. [26]

    In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

    Fast-ppr: Scaling personalized pagerank estimation for large graphs. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 1436–1445. doi:10.1145/2623330.2623745

    work page doi:10.1145/2623330.2623745

  27. [27]

    Yizhou Ma, Xikun Jiang, Evan W Wu, Luis-Daniel Ibáñez, and Jian Shi. 2024. Model-based data markets: a multi-broker game theoretic approach. In2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2293–2301

    2024

  28. [28]

    Xianfu Meng and Dongxu Liu. 2016. GeTrust: A guarantee-based trust model in chord-based P2P networks.IEEE Transactions on Dependable and Secure Comput- ing15, 1 (2016), 54–68. doi:10.1109/TDSC.2016.2530720

    work page doi:10.1109/tdsc.2016.2530720 2016

  29. [29]

    Xianfu Meng and Ge Zhang. 2020. TrueTrust: a feedback-based trust management model without filtering feedbacks in P2P networks.Peer-to-Peer Networking and Applications13 (2020), 175–189. doi:10.1007/s12083-019-00742-2

    work page doi:10.1007/s12083-019-00742-2 2020

  30. [30]

    Panagiotis Metaxas. 2009. Enhancing information reliability through backwards propagation of distrust.International Journal on Advances in Security Volume 2, Numbers 2&3, 2009(2009)

    2009

  31. [31]

    Thanh Linh Nguyen, Lam Nguyen, Thong Hoang, Dilum Bandara, Qin Wang, Qinghua Lu, Xiwei Xu, Liming Zhu, and Shiping Chen. 2025. Blockchain- empowered trustworthy data sharing: Fundamentals, applications, and challenges. Comput. Surveys57, 8 (2025), 1–36. doi:10.1145/3718082

    work page doi:10.1145/3718082 2025

  32. [32]

    1999.The PageRank citation ranking: Bringing order to the web.Technical Report

    Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999.The PageRank citation ranking: Bringing order to the web.Technical Report. Stanford infolab

    1999

  33. [33]

    Josiane Xavier Parreira, Carlos Castillo, Debora Donato, Sebastian Michel, and Gerhard Weikum. 2008. The juxtaposed approximate pagerank method for robust pagerank approximation in a peer-to-peer web search network.The VLDB Journal 17 (2008), 291–313. doi:10.1007/s00778-007-0057-y

    work page doi:10.1007/s00778-007-0057-y 2008

  34. [34]

    Josiane Xavier Parreira, Debora Donato, Sebastian Michel, and Gerhard Weikum

  35. [35]

    InProceedings of the 32nd international conference on Very large data bases

    Efficient and decentralized pagerank approximation in a peer-to-peer web search network. InProceedings of the 32nd international conference on Very large data bases. 415–426

  36. [36]

    Dimitrios Rafailidis and Fabio Crestani. 2017. Learning to rank with trust and distrust in recommender systems. InProceedings of the eleventh ACM conference on recommender systems. 5–13. doi:10.1145/3109859.3109879

    work page doi:10.1145/3109859.3109879 2017

  37. [37]

    Matthew Richardson, Rakesh Agrawal, and Pedro Domingos. 2003. Trust man- agement for the semantic web. InInternational semantic Web conference. Springer, 351–368. doi:10.1007/978-3-540-39718-2_23

    work page doi:10.1007/978-3-540-39718-2_23 2003

  38. [38]

    Zhiyuan Su, Ling Liu, Mingchu Li, Xinxin Fan, and Yang Zhou. 2015. Reliable and resilient trust management in distributed service provision networks.ACM Transactions on the Web (TWEB)9, 3 (2015), 1–37. doi:10.1145/2754934

    work page doi:10.1145/2754934 2015

  39. [39]

    Ugur Eray Tahta, Sevil Sen, and Ahmet Burak Can. 2015. GenTrust: A genetic trust management model for peer-to-peer systems.Applied Soft Computing34 (2015), 693–704. doi:10.1016/j.asoc.2015.04.053

    work page doi:10.1016/j.asoc.2015.04.053 2015

  40. [40]

    Chenchen Tan, Xinghao Li, Longxiang Gao, Tom H Luan, Youyang Qu, Yong Xiang, and Rongxing Lu. 2023. Digital twin enabled remote data sharing for internet of vehicles: System and incentive design.IEEE Transactions on Vehicular Technology72, 10 (2023), 13474–13489. doi:10.1109/TVT.2023.3275591

    work page doi:10.1109/tvt.2023.3275591 2023

  41. [41]

    Patricia Victor, Chris Cornelis, Ankur M Teredesai, and Martine De Cock. 2008. Whom should I trust? The impact of key figures on cold start recommendations. InProceedings of the 2008 ACM symposium on Applied computing. 2014–2018. doi:10.1145/1363686.1364174

    work page doi:10.1145/1363686.1364174 2008

  42. [42]

    Patricia Victor, Nele Verbiest, Chris Cornelis, and Martine De Cock. 2013. En- hancing the trust-based recommendation process with explicit distrust.ACM Transactions on the Web (TWEB)7, 2 (2013), 1–19. doi:10.1145/2460383

    work page doi:10.1145/2460383 2013

  43. [43]

    Monique V Vieira, Bruno M Fonseca, Rodrigo Damazio, Paulo B Golgher, Davi de Castro Reis, and Berthier Ribeiro-Neto. 2007. Efficient search ranking in social networks. InProceedings of the sixteenth ACM conference on Conference on information and knowledge management. 563–572. doi:10.1145/1321440.1321520

    work page doi:10.1145/1321440.1321520 2007

  44. [44]

    Wenbo Wu and George Konstantinidis. 2025. Trust and Reputation in Data Sharing: A Survey. arXiv:2508.14028 [cs.SI] https://arxiv.org/abs/2508.14028

    arXiv 2025

  45. [45]

    Li Xiong and Ling Liu. 2004. Peertrust: Supporting reputation-based trust for peer-to-peer electronic communities.IEEE transactions on Knowledge and Data Engineering16, 7 (2004), 843–857. doi:10.1109/TKDE.2004.1318566

    work page doi:10.1109/tkde.2004.1318566 2004

  46. [46]

    Jie Yin, Yang Xiao, Jie Feng, Mengmeng Yang, Qingqi Pei, and Xun Yi. 2025. DidTrust: Privacy-preserving Trust Management for Decentralized Identity.IEEE Transactions on Dependable and Secure Computing(2025). doi:10.1109/TDSC.2024. 3524760

    work page doi:10.1109/tdsc.2024 2025

  47. [47]

    Runfang Zhou and Kai Hwang. 2007. Powertrust: A robust and scalable reputation system for trusted peer-to-peer computing.IEEE Transactions on parallel and distributed systems18, 4 (2007), 460–473. doi:10.1109/TPDS.2007.1021

    work page doi:10.1109/tpds.2007.1021 2007

  48. [48]

    Runfang Zhou, Kai Hwang, and Min Cai. 2008. Gossiptrust for fast reputation aggregation in peer-to-peer networks.IEEE transactions on knowledge and data engineering20, 9 (2008), 1282–1295. doi:10.1109/TKDE.2008.48 Enforcing Trust Accountability with Backward Propagation KDD ’26, August 09–13, 2026, Jeju Island, Republic of Korea A Proof of Convergence We ...

    work page doi:10.1109/tkde.2008.48 2008