Time series data has a wide range of real-life applications, including financial forecasting, health monitoring, and social media analysis. However, this data often contains substantial individual privacy, and directly releasing them may lead to privacy infringement. Although existing research has proposed methods based on centralized differential privacy or local differential privacy to protect the privacy of time series data by injecting noise into the data, these methods may not be feasible in value-critical applications. Our previous work has introduced the TLDP model, which perturbs temporal data to ensure privacy guarantee while retaining original values. However, challenges persist when applying these methods to data exhibiting spatiotemporal correlations, such as trajectories. This paper proposes a novel trajectory release framework, Region-Aware Trajectory Re-sorting (RATR), which builds upon the temporal perturbation mechanism of TLDP by further introducing region-aware reachable constraints based on real road networks. In this way, it ensures privacy while significantly improving the spatial data utility. We construct region-aware reachable constraints and design a re-sorting algorithm to optimize the perturbed trajectories. Extensive experiments conducted on three real-world datasets demonstrate that our method significantly outperforms existing state-of-the-art methods in terms of data utility while maintaining a lower overall cost.

Item mining, a fundamental task for collecting statistical data from users, has raised increasing privacy concerns. To address these concerns, local differential privacy (LDP) was proposed as a privacy-preserving technique. Existing LDP item mining mechanisms primarily concentrate on global statistics, i.e., those from the entire dataset. Nevertheless, they fall short of usertailored tasks such as personalized recommendations, whereas classwise statistics can improve task accuracy with fine-grained information. Meanwhile, the introduction of class labels brings new challenges. Label perturbation may result in invalid items for aggregation. To this end, we propose frameworks for multi-class item mining, along with two mechanisms: validity perturbation to reduce the impact of invalid data, and correlated perturbation to preserve the relationship between labels and items. We also apply these optimized methods to two multi-class item mining queries: frequency estimation and top-$k$item mining. Through theoretical analysis and extensive experiments, we verify the effectiveness and superiority of these methods.

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ω-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes.

With the prevalence of mobile computing, mobile devices have been generating numerous sensor data, a.k.a., time series. Since these time series may include sensitive information, users are posed with severe privacy risks. To protect individuals' privacy, local differential privacy (LDP) is proposed. However, the added noise satisfying LDP typically degrades the utility of released data, especially for anomaly detection such as healthcare monitoring and hazard alarming. In this paper, we study privacy-preserving time series release with anomalies. Recently, local differential privacy in the temporal setting (TLDP) is proposed to perturb the temporal order rather than the values. While it improves the utility for releasing value-critical data, it still suffers from low utility for anomaly detection, because of the inevitable missing and delayed values incurred by TLDP perturbation. We propose to improve its utility from two aspects. To reduce the missing values, we utilize selective substitution according to items' anomaly scores. To decrease the delayed values, we define metric-based$(\alpha , \delta )$-TLDP and propose a mechanism that can prioritize anomaly release at a close timestamp while still guaranteeing the same TLDP privacy. Through theoretical and empirical evaluation, we show superior performance gain over existing TLDP-based mechanisms on both synthetic and real-world datasets.