| Mobile crowdsensing technology,by breaking the constraints of time and space,achieves real-time,large-scale crowdsensing data sensing,transmission,and sharing.However,its application in secure data sharing faces challenges in various areas such as user privacy,data retrieval efficiency,data access overhead,forward security,and the trustworthiness of cloud servers.Specifically,in the mobile crowdsensing scenario,the data access process might lead to the leakage of sensitive user information,posing threats to user privacy.Current data retrieval methods lack efficiency and there is an absence of a proficient mechanism for targeted data,affecting the overall service quality and user experience.To ensure data integrity and security,complex encryption operations are often introduced,increasing the complexity and cost of data access.This problem of expensive data access overhead becomes more acute on resource-constrained mobile devices.Moreover,the forward security of the data deserves attention as,once the security mechanism is compromised,all data,including historical records,are at risk of exposure.Furthermore,user data is often stored on cloud servers,and both the data stored in the cloud and the user’s access permissions undergo dynamic updates,necessitating data processing by the cloud service.However,threats such as hacking and internal abuse highlight the growing issue of cloud server unreliability.To address these issues,secure data sharing in mobile crowdsensing needs to introduce advanced encryption and anonymity technologies to protect user privacy,optimize the data retrieval mechanism to improve efficiency,mitigate data access overhead,enhance forward security of data,and augment the safety and reliability of cloud servers.Implementing these improvements will help unleash the potential of mobile crowdsourcing in secure data sharing,further promoting its applications across various domains.This dissertation deeply studies identity privacy,lightweight access,forward security,dynamic authorization in mobile crowdsensing data sharing scenarios:(1)This dissertation first introduced a privacy-preserving task allocation scheme in the mobile crowdsensing scenario,which allows the requester to share task messages with multiple task executors through a single encryption.This ensures rapid location and access to target data without disclosing the requester’s identity.To further improve the speed of target data access,a privacy-preserving task allocation scheme was introduced for Io Tcentric mobile crowdsensing,which offers lightweight decryption operations,reducing communication and computation overheads.Rigorous security proofs were provided,and comparisons in terms of function and performance with similar schemes were conducted,proving the efficiency and viability of the proposed schemes.(2)This dissertation presented an edge-assisted pierceable fine-grained task allocation scheme oriented towards Io T crowdsensing,which provides efficient one-to-many secure data sharing and offloads most computational overheads from resource-constrained terminal devices to edge devices,while the requester(decryptor)only needs to perform simple decryption operations.The forward security of the scheme is ensured by integrating bloom filters to facilitate key updates.Finally,rigorous security proofs and analysis were provided,demonstrating its security.The feasibility and practicality of the scheme were evidenced through theoretical comparisons and experimental results.(3)This dissertation introduced an efficient decentralized attribute-hidden data sharing scheme for mobile crowdsensing scenarios,which addresses unauthorized data access,privacy leakage of access control,key escrow of single-authority,and high access overhead.Not only does it enable crowdsensing users to specify attribute-based access control for encrypting crowdsensing data,but also it allows multiple authorities to co-generate private keys for crowdsensing users,ensuring no single authority can impersonate legitimate users to access target crowdsensed data illicitly.The proposed scheme also allowed for rapid access to target crowdsensing data without revealing access control attribute privacy.Rigorous security proofs and performance analysis were presented,demonstrating the capability of the scheme to ensure efficient mobile crowdsensing data sharing.(4)This dissertation put forward a verifiable delegated decryption for secure broadcasting for mobile crowdsensing,which enables the cloud,with the requester’s authorization,to transform the original ciphertext into broadcast-based re-encrypted ciphertext,allowing multiple collaborating executors to access a single task and achieve cross-domains dynamic authorization.To prevent untrusted clouds from fabricating re-encrypted ciphertexts to mislead collaborating executors,third-party verifiers are allowed to inspect the correctness of the re-encryption steps.Rigorous security proofs were provided,and a comparison in terms of function and performance with similar schemes was conducted,highlighting the efficiency of the proposed scheme. |
