Smart Contract Coordinated Privacy Preserving Crowd-Sensing Campaigns

• URL: http://arxiv.org/abs/2408.10648v1
• Date: Tue, 20 Aug 2024 08:41:57 GMT
• Title: Smart Contract Coordinated Privacy Preserving Crowd-Sensing Campaigns
• Authors: Luca Bedogni, Stefano Ferretti,
• Abstract summary: Crowd-sensing has emerged as a powerful data retrieval model, enabling diverse applications by leveraging active user participation. Traditional methods like data encryption and anonymization, while essential, may not fully address these issues. This paper proposes a system utilizing smart contracts and blockchain technologies to manage crowd-sensing campaigns.
• Score: 4.204990010424083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Crowd-sensing has emerged as a powerful data retrieval model, enabling diverse applications by leveraging active user participation. However, data availability and privacy concerns pose significant challenges. Traditional methods like data encryption and anonymization, while essential, may not fully address these issues. For instance, in sparsely populated areas, anonymized data can still be traced back to individual users. Additionally, the volume of data generated by users can reveal their identities. To develop credible crowd-sensing systems, data must be anonymized, aggregated and separated into uniformly sized chunks. Furthermore, decentralizing the data management process, rather than relying on a single server, can enhance security and trust. This paper proposes a system utilizing smart contracts and blockchain technologies to manage crowd-sensing campaigns. The smart contract handles user subscriptions, data encryption, and decentralized storage, creating a secure data marketplace. Incentive policies within the smart contract encourage user participation and data diversity. Simulation results confirm the system's viability, highlighting the importance of user participation for data credibility and the impact of geographical data scarcity on rewards. This approach aims to balance data origin and reduce cheating risks.

Related papers

• Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems [11.544642210389894]
  Federated recommender systems have been enhanced through data sharing and continuous model updates. Given the sensitivity of IoT data, transparent data processing in data sharing and model updates is paramount. Existing methods fall short in tracing the flow of shared data and the evolution of model updates. We present LIBERATE, a privacy-traceable federated recommender system.
  arXiv  Detail & Related papers  (2024-06-07T07:21:21Z)
• Improving Privacy-Preserving Techniques for Smart Grid using Lattice-based Cryptography [1.4856472820492366]
  SPDBlock is a blockchain-based solution ensuring privacy, integrity, and resistance to attacks. It detects and prosecutes malicious entities while efficiently handling multi-dimensional data transmission. Performance tests reveal SPDBlock's superiority in communication and computational efficiency over traditional schemes.
  arXiv  Detail & Related papers  (2024-04-17T19:51:52Z)
• Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand [57.82021900505197]
  Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. We propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets.
  arXiv  Detail & Related papers  (2024-03-29T14:58:28Z)
• Personalized Federated Learning with Attention-based Client Selection [57.71009302168411]
  We propose FedACS, a new PFL algorithm with an Attention-based Client Selection mechanism. FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions. Experiments on CIFAR10 and FMNIST validate FedACS's superiority.
  arXiv  Detail & Related papers  (2023-12-23T03:31:46Z)
• Privacy-Preserving Hierarchical Anonymization Framework over Encrypted Data [0.061446808540639365]
  This study proposes a hierarchical k-anonymization framework using homomorphic encryption and secret sharing composed of two types of domains. The experimental results show that connecting two domains can accelerate the anonymization process, indicating that the proposed secure hierarchical architecture is practical and efficient.
  arXiv  Detail & Related papers  (2023-10-19T01:08:37Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior [0.0]
  This paper proposes a comprehensive framework that integrates data trust in federated learning with InterPlanetary File System, blockchain, and smart contracts. The proposed model is effective in improving the accuracy of federated learning models while ensuring the security and fairness of the data-sharing process. The research paper also presents a decentralized federated learning platform that successfully trained a CNN model on the MNIST dataset.
  arXiv  Detail & Related papers  (2023-07-19T23:05:49Z)
• Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
  We design an intelligent approach to online privacy protection that leverages supervised learning. By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
  arXiv  Detail & Related papers  (2023-04-06T05:20:16Z)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• Mitigating Leakage from Data Dependent Communications in Decentralized Computing using Differential Privacy [1.911678487931003]
  We propose a general execution model to control the data-dependence of communications in user-side decentralized computations. Our formal privacy guarantees leverage and extend recent results on privacy amplification by shuffling.
  arXiv  Detail & Related papers  (2021-12-23T08:30:17Z)
• Second layer data governance for permissioned blockchains: the privacy management challenge [58.720142291102135]
  In pandemic situations, such as the COVID-19 and Ebola outbreak, the action related to sharing health data is crucial to avoid the massive infection and decrease the number of deaths. In this sense, permissioned blockchain technology emerges to empower users to get their rights providing data ownership, transparency, and security through an immutable, unified, and distributed database ruled by smart contracts.
  arXiv  Detail & Related papers  (2020-10-22T13:19:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.