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.

Differential privacy (DP) has become a prevalent privacy model in a wide range of machine learning tasks, especially after the debut of DP-SGD. However, DP-SGD, which directly perturbs gradients in the training iterations, fails to mitigate the negative impacts of noise on gradient direction. As a result, DP-SGD is often inefficient. Although various solutions (e.g., clipping to reduce the sensitivity of gradients and amplifying privacy bounds to save privacy budgets) are proposed to trade privacy for model efficiency, the root cause of its inefficiency is yet unveiled. In this work, we first generalize DP-SGD and theoretically derive the impact of DP noise on the training process. Our analysis reveals that, in terms of a perturbed gradient, only the noise on direction has eminent impact on the model efficiency while that on magnitude can be mitigated by optimization techniques, i.e., fine-tuning gradient clipping and learning rate. Besides, we confirm that traditional DP introduces biased noise on the direction when adding unbiased noise to the gradient itself. Overall, the perturbation of DP-SGD is actually sub-optimal from a geometric perspective. Motivated by this, we design a geometric perturbation strategy GeoDP within the DP framework, which perturbs the direction and the magnitude of a gradient, respectively. By directly reducing the noise on the direction, GeoDP mitigates the negative impact of DP noise on model efficiency with the same DP guarantee. Extensive experiments on two public datasets (i.e., MNIST and CIFAR-10), one synthetic dataset and three prevalent models (i.e., Logistic Regression, CNN and ResNet) confirm the effectiveness and generality of our strategy.

The spatio-temporal (ST) mobility patterns derived from trajectory data are crucial for applications such as location-based services and urban analytics. However, releasing these mobility features raises significant privacy concerns, as they may expose sensitive personal location information. Differential privacy (DP) is widely used to safeguard individual privacy during data releases, but existing methods for releasing ST features often suffer from utility loss because their high dimensionality requires injecting substantial noise to meet privacy guarantees. Several recent approaches attempt to address this issue by reducing noise in differentially private spatio-temporal (DPST) features, but they either discard valuable information while compressing noisy data representations or rely solely on restrictive road network topology constraints, resulting in only modest utility improvements. In this paper, we present DPDeno, a post-processing framework designed to significantly enhance the utility of DPST features. First, DPDeno generates synthetic trajectory datasets using public information (e.g., road network data) and applies existing DP methods to create paired DPST (noisy) and ST (clean) features. It then trains a spatio-temporal graph autoencoder (STGAE), which models each feature as a graph, with road segments as nodes and transitions over time as edges. By minimizing node- and edge-level reconstruction losses between the noisy and clean pairs, STGAE learns to refine DPST inputs toward the structural consistency of their clean counterparts, thereby improving their practical utility. The trained model is then used to post-process real DPST features. Importantly, DPDeno preserves the original DP guarantee, as STGAE is trained solely on synthetic data generated from public sources without accessing any private information. Experimental results on two real-world trajectory datasets show that DPDeno significantly improves both the statistical accuracy and practical usability of released mobility features.

Recently, federated learning (FL) has become a prevalent algorithm to harvest data while preserving privacy. However, private information can still be compromised by local parameters during transmissions between local parties and the central server. To address this problem, local differential privacy (LDP) has been adopted. Known as federated LDP-SGD, each local device only sends perturbed parameters to the central server. However, due to the low model efficiency caused by overwhelming LDP noise, only a relaxed LDP privacy scheme, namely Gaussian mechanism, is explored in the federated LDP-SGD literature. The objective of this paper is to enable other LDP mechanisms (e.g., Laplace, Piecewise, Square Wave and Gaussian) in federated learning by enhancing their model efficiency. We first propose an analytical framework that generalizes federated LDP-SGD and derives its model efficiency. Serving as a benchmark, this framework can compare performances of different LDP mechanisms in federated learning. Based on this framework, we identify a new perspective to generally optimize federated LDP-SGD, namely, the vectorized perturbation strategyLDPVec. By only perturbing the direction of a gradient,LDPVecbetter preserves the descending direction of the gradient, which consequently leads to comprehensive efficiency improvements in terms of various LDP mechanisms.

The spatial density distribution collected and aggregated from users’ trajectory data is vital for location-based services like regional popularity analysis and congestion measurement. However, spatial density aggregation poses privacy concerns since trajectory data usually originate from users. Local differential privacy (LDP) addresses these concerns by allowing users to perturb their data before reporting it. Yet, LDP is vulnerable to poisoning attacks where attackers manipulate data from malicious users. Recent studies attempt to defend against such attacks in LDP-enabled frequency estimation but suffer from inaccurate data recovery due to empirical presets of malicious user proportions and inaccurate malicious data estimation. These issues worsen in spatial density aggregation, as high-dimensional trajectory data help conceal malicious information. In this work, we propose GeoRecover, a method to defend against poisoning attacks in LDP-enabled spatial density aggregation by addressing previous limitations. GeoRecover designs an adaptive model to unify these attacks. Under this model, GeoRecover estimates the proportion of malicious users using statistical differences between genuine and malicious data and learns malicious data statistics through LDP properties. This allows GeoRecover to recover accurate spatial density distribution by subtracting malicious users’ contributions. Evaluations on two real-world datasets show GeoRecover outperforms state-of-the-art methods in recovery accuracy, defense capability, and practical performance.

Local differential privacy (LDP), which enables an untrusted server to collect aggregated statistics from distributed users while protecting the privacy of those users, has been widely deployed in practice. However, LDP protocols for frequency estimation are vulnerable to poisoning attacks, in which an attacker can poison the aggregated frequencies by manipulating the data sent from malicious users. Therefore, it is an open challenge to recover the accurate aggregated frequencies from poisoned ones. In this work, we propose LDPRecover, a method that can recover accurate aggregated frequencies from poisoning attacks, even if the server does not learn the details of the attacks. In LDPRecover, we establish a genuine frequency estimator that theoretically guides the server to recover the frequencies aggregated from genuine users' data by eliminating the impact of malicious users' data in poisoned frequencies. Since the server has no idea of the attacks, we propose an adaptive attack to unify existing attacks and learn the statistics of the malicious data within this adaptive attack by exploiting the properties of LDP protocols. By taking the estimator and the learning statistics as constraints, we formulate the problem of recovering aggregated frequencies to approach the genuine ones as a constraint inference (CI) problem. Consequently, the server can obtain accurate aggregated frequencies by solving this problem optimally. Moreover, LDPRecover can serve as a frequency recovery paradigm that recovers more accurate aggregated frequencies by integrating attack details as new constraints in the CI problem. Our evaluation on two real-world datasets, three LDP protocols, and untargeted and targeted poisoning attacks shows that LDPRecover is both accurate and widely applicable against various poisoning attacks.

Valuable information and knowledge can be learned from users’ location traces and support various location-based applications such as intelligent traffic control, incident response, and COVID-19 contact tracing. However, due to privacy concerns, no authority could simply collect users’ private location traces for mining or even publishing. To echo such concerns, local differential privacy (LDP) enables individual privacy by allowing each user to report a perturbed version of their data. Unfortunately, when applied to location traces, LDP cannot preserve the semantics in the context of location traces because it treats all locations (i.e., various points of interest) as equally sensitive. This results in a low utility of LDP mechanisms for collecting location traces. In this paper, we address the challenge of collecting and sharing location traces with valuable semantics while providing sufficient privacy protection for participating users. We first propose semantic-constrained local differential privacy (SLDP), a new privacy model to provide a provable mathematical privacy guarantee while preserving desirable semantics. Then, we design a location trace perturbation mechanism (LTPM) that users can use to perturb their traces in a way that satisfies SLDP. Finally, we propose a private location trace synthesis (PLTS) framework in which users use LTPM to perturb their traces before sending them to the collector, who aggregates the users’ perturbed data to generate location traces with valuable semantics. Extensive experiments on three real-world datasets demonstrate that our PLTS outperforms existing state-of-the-art methods by at least 21% in a range of real-world applications, such as spatial visiting queries and frequent pattern mining, under the same privacy leakage.

While collecting data from a large population, local differential privacy (LDP), which only sends users' perturbed data to the data collector, becomes a popular solution to preserving each user's privacy. However, as high-dimensional data collection becomes prevalent for machine learning, LDP suffers from low utility (a.k.a., the dimensionality curse) as its privacy budget in each dimension is severely diluted. In a previous work [1], we proposed an analytical framework for benchmarking various LDP mechanisms and a re-calibration protocol for its utility enhancement in high-dimensional space. However, they have several limitations, including difficulty in setting a suitable benchmark parameter (i.e., the probabilistic supremum of deviation), a mismatch of the metric with prevalent experimental metrics, and costly re-benchmarking operation upon population change. In this paper, we propose a toolbox LDPTube to address these issues. It first consists of a non-parametric benchmark in high-dimensional space, which adopts MSE as the metric and avoids re-benchmarking upon population change. Then we adapt this benchmark to personalized LDP, where each user can choose her own privacy budget and privacy region. Last but not the least, we enhance the re-calibration protocol in [1] by an adaptive protocolHDR4ME*that opportunistically chooses suitable regularization terms that can maximize utility. We verify the correctness and effectiveness of these new solutions by both theoretical analysis and experimental results.

Vehicle trajectory data is essential for traffic management and location-based services. However, publishing real-life trajectory data has been challenging because vehicle trajectories contain users’ sensitive information. Differential privacy addresses such problems by publishing a synthetic version of the input dataset, but existing works always assume the real-world data is absolutely accurate. This assumption no longer holds in trajectory data because it typically contains errors due to inaccurate positioning services, which leads to poor performance of data synthesized by such trajectories. Even worse, existing works may generate unrealistic trajectories due to their coarse data synthesis methods, resulting in low practical utility or even inability to handle complex tasks. In this paper, we propose aPrivacy-preserving andUtility-enhancing framework forTrajectorySynthesization (PUTS). Our framework mitigates the impact of data errors in trajectories on differential privacy mechanisms, by exploiting map-matching techniques and real-world road network structure. InPUTS, a two-layer approach from path to trajectory synthesis is proposed to not only guarantee the reality of synthetic trajectories, but also scale upPUTSin real-world applications. Extensive experiments on real-world datasets show thatPUTSsignificantly outperforms existing methods in terms of utility in a range of real-world applications.

Image classification using convolutional neural networks (CNNs) is critical for broader industrial applications like defect detection. To protect sensitive data during the industrial process, increasing institutions are highly interested in training CNN classifiers collaboratively with federated learning (FL). However, the existing FL solutions cannot address the sample deficiency and heterogeneous learning resource issues at different practical institutions. In this article, we present a federated lightweight relation network (FLRN), a lightweight industrial image classifier based on our federated few-shot learning (FFSL) architecture. Results of extensive experiments considering different real-world FFSL scenarios indicate that, unlike the state-of-the-art few-shot learning method relation network (RN), the FLRN performs well on not only FL participants with mutually isolated classes of samples but also external institutions with limited samples from unseen classes. Compared to the RN with the predominating FedAvg-based FL deployment, the FLRN manages to achieve as low as$\text{29.6}\times$less client–cloud communication,$\text{5.2}\times$less computation, and$\text{22.0}\times$less storage costs of clients.

Vehicle trajectory data is critical for traffic management and location-based services. However, the released trajectories raise serious privacy concerns because they contain sensitive information such as homes and workplaces. Based on differential privacy, this problem can be addressed by generating synthetic trajectories from the original sensitive data while guaranteeing personal privacy. Unfortunately, existing methods focus on synthesizing trajectory datasets that preserve summary-level statistics (e.g., the overall distribution of user movements), making these synthetic trajectories lose individual-level mobility patterns. As shown in our experiment, this results in the low performance of their synthetic datasets in real-world applications. To address these limitations, we propose a novel solution for Synthesizing Private and Realistic Trajectories, namely SPRT, whose key idea is to integrate the public geography structures of the target area into the process of private trajectory synthesis. This enables us to capture more accurate mobility patterns to synthesize realistic trajectories, which can preserve both summary-level statistics and individual-level mobility behaviors. Consequently, the synthetic trajectories generated by SPRT are more similar to real trajectories and therefore more practical. We evaluate the performance of SPRT in real-world applications by applying its synthetic data to a series of trajectory analytic tasks. The results demonstrate that our solution improves data utility by at least 37% over state-of-the-art approaches.