In spatial crowdsourcing services, the trajectories of the workers are sent to a central server to provide more personalized services. However, for the honest-but-curious servers, it also poses a challenge in terms of potential privacy leakage of the workers. Local differential privacy (LDP) is currently the latest technique to protect data privacy. However, most of LDP-based schemes have limitations in providing good utility due to extensive noise in perturbing trajectories. In this work, to balance the privacy and utility, we propose a novel pattern-aware privacy protection method called trajectory-aware privacy-preserving with local differential privacy (TALDP). The key idea is that, rather than applying the same degree of perturbation to all location points, we employ adaptive privacy budget allocation, assigning varied privacy budgets to individual location points, thereby mitigating the perturbation’s impact and enhancing overall utility. Meanwhile, to ensure the privacy, we give the different perturbing points to different privacy budgets according to their important degree for the patterns of the trajectories. In particular, we use Karman filter method to select the important location points and decide their privacy budgets. We conduct extensive experiments on three real datasets. The results show that our approach improves the utility over many other current methods while still provide good the privacy protection.

The proliferation and application of the Internet of Things (IoT) have significantly increased the production and processing of large volumes of images. Due to privacy concerns, such data are often encrypted before being outsourced to third parties for subsequent retrieval and processing. Existing encrypted image retrieval schemes primarily aim to improve search accuracy but are limited in terms of retrieval efficiency and storage expenses, rendering them challenging to be implemented on IoT devices. To address these issues, we propose a segmented hash-based privacy-preserving image retrieval (SHPIR) scheme that enables accurate and efficient retrieval of encrypted images in IoT networks. First, we propose a feature extraction model based on the convolutional neural network (CNN) to generate low-dimensional segmented hash codes that represent both image categories and features accurately. Next, we design the cryptographic hierarchical index structure based on the segmented hash codes to improve retrieval efficiency and accuracy. We also design a secure Hamming distance computation algorithm and employ the learning with errors (LWEs)-based secure k-nearest neighbor (kNN) algorithm to preserve the privacy of feature vectors and file-access patterns. Finally, we provide a security analysis verifying that the SHPIR scheme can protect image privacy as well as indexing and query privacy. Additionally, we constructed a real IoT environment to test the practical effectiveness and applicability of our scheme. Experimental evaluation indicates that our proposed scheme outperforms existing state-of-the-art schemes in retrieval accuracy, search efficiency, and storage costs, making it more suitable for real-world IoT applications.

Unlike general routing strategies, network coding (NC) can combine encoding functions with multi-path propagation over a network. This allows network capacity to be achieved to support complex security solutions. Moreover, NC has intrinsic security advantages against passive attacks over traditional routing techniques. However, due to the transmission of the global encoding kernels, the system is fragile to eavesdropping attacks with multiple probes. This paper proposes a generic unicast secure transmission scheme based on random linear network coding (RLNC). Specifically, the intended receiver generates a random matrix upon receiving the request from the source node, and then transmits each row vector of this matrix over a link reversely to the source node. Each intermediate node rearranges all received vectors to form a matrix by row, and then post-multiplies its local encoding kernel by this matrix to obtain a new matrix. Similarly, each row vector of the new matrix is reversely transmitted over a link to the source node. This procedure is performed until we have the source node, where the generalized inverse of the received matrix (or part of it) can be used as its local encoding kernel. Hence, the intended receiver can use the generated matrix (or the corresponding part) to decode the received data packets directly. We also analyze the security to demonstrate that the proposed scheme is at least as secure as other methods against wiretapping attacks. We also evaluate the performance of the proposed scheme to demonstrate its utility.