Local differential privacy (LDP) provides lightweight and provable privacy protection and has wide applications in private data collection. Key-value data, as a popular NoSQL structure, requires simultaneous frequency and mean estimations of each key, which poses a challenge to traditional LDP-based collection methods. Despite many schemes proposed for the privacy protection of key-value data, they inadequately solve the condensed perturbation for keys and the advanced combination of privacy budgets, leading to suboptimal estimation accuracy. To address this issue, we propose an efficient key-value collection scheme (COKV) with tight privacy budget composition. In our scheme, we first design a padding and sampling protocol for key-value data to avoid privacy budget splitting. Second, to enhance the utility of key perturbation, we design a key perturbation primitive and optimize the perturbation range to improve computational efficiency. After that, we propose a key-value association perturbation algorithm whose value perturbation strategy guarantees the output expectation equals the original value. Finally, we demonstrate that through a tight privacy budget composition, COKV can provide higher data utility under the same privacy level. Theoretical analysis shows that COKV possesses lower frequency and mean estimations variance. Extensive experiments on both synthetic and real-world datasets also indicate that COKV outperforms the current state-of-the-art methods for secure key-value data collection.

Machine learning-based trajectory-user linking (TUL) task, which explores human mobility patterns and identifies known users from anonymous trajectories, is widely applied in location-based personalized recommendation systems. However, TUL models implicitly and inadvertently retain users historical trajectories in training data, which could be revealed under malicious inference and analysis. To address this issue, we propose UNIRE, a knowledge transfer-style TUL model aggregation framework to protect sensitive training data. Specifically, teacher TUL models are trained with private users trajectories and then utilized to collaboratively annotate a sensitive-trajectories-isolated dataset. Leveraging this teacher-labeled dataset, student TUL model can be trained and released as the secure aggregation of teacher models, preventing attackers from arbitrarily accessing or exposing the private trajectories. Meanwhile, differential privacy is applied for safeguarding the knowledge transfer process, ensuring that aggregation information from individual teacher models is indistinguishable. Furthermore, we introduce trajectory Equalization and Pseudo Classification as data alignment and accuracy optimization mechanisms for training and aggregating, respectively. Finally, security analysis and extensive experiments indicate that UNIRE framework achieves effective sensitive training data preservation and performance-improved TUL models aggregation, compared to state-of-the-art baselines.

As a straightforward consequence of advances in the Internet of Things (IoT), location-based service (LBS) applications have been pervasive in our daily lives. Nevertheless, since those LBS applications will continuously collect and disclose users’ location data, major concerns on privacy leakage are raised. Aiming at the challenge, in this article, we first build up a detect module (DM) and employ it to investigate more than 80% of LBS applications are keen on tracking users. Then, to thwart the threats from those LBS applications, we exploit the deceptive dummy techniques and design a dummy-based location privacy preserving scheme, named dummy location provider (DLP), which comprises three algorithms, namely, Spread, Shift, and Switch. Specifically, Spread and Shift are in charge of generating deceptive dummies and trajectories. And with Switch, users’ real locations are replaced with dummy trajectories before being submitted to LBS applications. As a result, users can not only prevent applications from accessing location data arbitrarily, but also avoid being questioned by applications in terms of honesty. Furthermore, to guarantee necessary functions of LBS, DLP offers customizable privacy-preserving strategies for users, which can achieve flexible location data usage control. Finally, our DLP can also attain achievable and effortless deployment over smart devices. Detailed security analysis indicates that DLP resists inference attacks even facing skeptical applications. In addition, for performance evaluation, a DLP application (DLPA) is developed on the Android platform and tested in the real environment, and the extensive experimental results demonstrate that the DLPA is indeed effective and high efficiency in practice.