Yuqing Wang

Graph neural networks (GNNs) are extensively employed in location-related scenarios, relying on aggregation to gather features from neighboring nodes based on edge weights. Features are closely bound to nodes’ locations and edge weights mirror distance correlations. In this sense, certain privacy concerns exist while providing location-based services if there is insufficient privacy protection. To this end, we propose a privacy-preserving and distance-aware data aggregation framework (PEDA) for GNNs. Specifically, PEDA achieves location privacy by combining circular-based positional coding with inner product functional encryption. Because of the masks in the codes, the decryption returns masked distances, preventing distance leakage. Following this, in order to protect feature privacy, we employ secret sharing. To preserve the collection strategy’s privacy, we implement an oblivious transfer for collecting the shared features. Additionally, we securely generate the adjacency matrix and aggregate features based on multi-party computation. Thorough security analysis and comprehensive evaluation demonstrate the privacy, feasibility and practicality of our approach. When compared to related works, PEDA offers four types of privacy, maintains distance awareness and feature utility, and allows for oblivious data collecting with little computational cost sacrifice.

The widespread use of machine learning in location-related scenarios is propelling the rapid development of intelligent transportation. To assist users in making more informed travel plans, the demand for improving prediction accuracy is growing. Prior to model training, data cleaning is a common method used to eliminate redundant, erroneous and outlier samples. However, in intelligent transportation, there are serious issues with location awareness and privacy protection of existing data cleaning schemes. Therefore, we propose a location-aware and privacy-preserving data cleaning framework (PriSPA) which provides a cleaned dataset consisting of the samples from adopted data suppliers at qualified locations while ensuring the privacy of locations, spatial constraints and sensitive samples. We combine boolean secret sharing with XOR operations to make sure that it is possible to figure out whether a location complies with spatial constraints without leakage. More specifically, we ensure privacy using key agreement, secret sharing, authenticated encryption and random permutation. We seriously analyze the security of PriSPA and conduct comprehensive experiments to prove its security, effectiveness and efficiency. Based on the comparisons with the raw traffic forecasting framework, we observe that PriSPA improves the precision of the model with 17.6% -32.7% error reduction.

With the rapid development of the Internet of Vehicles (IoV), location-aware outsourcing data aggregation is evolving into a fundamentally key role for analyzing a significant amount of data among smart devices. Due to the spatial property of the data in IoV, location privacy and data security in outsourcing data aggregation face critical challenges. Meanwhile, because of the diversity of entities in IoV, how aggregating data from multiple domains is also a serious issue. In this paper, we propose a location-aware verifiable outsourcing data aggregation (LAVODA) for IoV where aggregators are hierarchical for the cross-chain mechanism in multi-blockchains. With homomorphic encryption and homomorphic commitment, we achieve the verifiability of the aggregation while ensuring data confidentiality. Specifically, we combine twin-DH with circle-based location verification to ensure the privacy of the requester’s location strategy and data providers’ locations. The security analysis shows that our scheme can achieve the above secure properties. In addition, the experiment results demonstrate that our scheme is efficient and feasible in practice.