Abstract Anomaly detection in multivariate time series is critical for system status monitoring, particularly in healthcare surveillance. However, centralized anomaly detection in cloud-edge collaborative environments may raise privacy concerns. This paper proposes a Federated Aggregation Optimization-based Privacy-Preserving Anomaly Detection Framework for Time Series (FAPAD). The FAPAD framework mitigates the risk of privacy leakage by distributing data anomaly detection tasks across multiple edge nodes and deploying model anomaly detection and parameter aggregation modules on the cloud. Specifically, we integrate a deep anomaly detection method for multivariate time-series data in the anomaly detection module to ensure detection performance. This method employs an adversarial Transformer feature learning model to capture the feature representations of the time-series data. Anomaly scores and decision thresholds are then calculated using an anomaly interpretation method to enable effective differentiation between normal and anomalous data. Furthermore, the differential privacy technique is employed to protect the privacy of both the model and data during the parameter upload phase, thus preventing model inference attacks. In the parameter aggregation module, a federated aggregation optimization strategy is designed, which combines the F 1 score and $$L_2$$ L 2 distance to assign aggregation weights to local model parameters, thereby reducing the impact of anomalous models on the global aggregation outcome. Extensive experiments conducted on four public datasets demonstrate the effectiveness of the proposed FAPAD framework.

Anomaly detection in multivariate time series is crucial to monitor system status, such as fault detection in industrial systems. However, detecting anomalies in multivariate time series is challenging due to few labels, complex spatiotemporal correlations, and ultrafast detecting demands. Existing anomaly detection methods rarely address these challenges simultaneously. Herein, we design an adversarial transformers-based unsupervised anomaly detection model (ATUAD). In ATUAD, a Transformer-based encoder–decoder is constructed to learn sequence features, and adversarial training is adopted to amplify mild anomalies and enhance the robustness. Besides, we propose a peak-over-threshold-based dynamic threshold mechanism to improve the anomaly detection performance of ATUAD by automatically determining the threshold. In addition, we provide an anomaly explanation method to help ATUAD pinpoint root causes for anomalies. Comparison experiments, ablation studies, and overhead analysis on public datasets show that ATUAD can outperform the state-of-the-art baseline methods.

vMixing coins strategy can realize the anonymity of user information, thereby protecting the user's privacy. Ideally, the blacklist is public information and all bad coins are recorded in it. However, due to the failure of some bad coins to be registered in the blacklist in time, users can only obtain part of the blacklist information, which allows illegal criminals to take advantage of it. How to prevent illegal activities under the partial information blacklist and how to design coins' quality updating rule rationally have become open issues in mixing coins. The updating rule of coins' quality in mixing is addressed since illegal criminals may carry out illegal activities, for example, money laundering. ImpSuic, an improved suicide strategy, is proposed as a new quality updating rule. The intuition is: all coins of the one who has the highest bad coins according to the blacklist, are recorded as bad coins. On the other hand, the coins' quality of others remain unchanged. Besides, linear programming is introduced into ImpSuic strategy to predict the maximum utility after mixing coins, which facilitates users to make reasonable decisions before mixing coins. Simulation results show that the quality updating rule in ImpSuic strategy can preserve users' privacy and antimoney launder.

Perpetrators leverage the untraceable feature to conduct illegal behaviors leading security issues with respect to mixing coins. Generally, bad coins are blocked based on a common blacklist. However, the blacklist may not be updated in time, which results in that bad coins escape the blocking. Consequently, perpetrators can still conduct illicit behaviors such as money laundering. In this paper, we apply game theory under imperfect information to study how coins' quality restrain these illicit behaviors under the incomplete scenario. More specifically, we propose a strategy for participants to submit deposits if they hope to mix coins with others even if they are not in blacklist at this time. The deposits will not be refunded when participants are included in the blacklist after mixing. Therefore, no participants have incentives to mix with bad coins. At the last part of this paper, we also simulate the incomes for participants, which indicates that deposits strategy is effective to prevent illicit behaviors.

This paper studies the cellular traffic offloading in a content sharing network (CSN) where some mobile devices functioned as caching contributors can provide the nearby devices with popular contents on demand via D2D communication links. In consideration of the inherent selfishness of mobile users, the main challenge turns to be how to urge the device to maximize content sharing actively acting as the contributory caching device (CCD) via D2D link. To facilitate content sharing, a contribution-based incentive mechanism is introduced to encourage the CCDs. We formulate selectively caching as a matching problem and put forward a caching strategy to determine whether to cache some particular contents using Kuhn-Munkres algorithm. And the performances are validated by the numerical results.

We propose a new unitary matrix code for differential space-time modulation that is useful for two transmit antennas and a large number of receive antennas. We optimize the design of this code with respect to the Euclidean distance criterion. To reduce decoding complexity, we derive two suboptimal low-complexity receivers that allow individual data symbols in the code to be sequentially decoded. Simulation results show that the new codes outperform some existing codes, and that the low-complexity receivers approach the performance of maximum-likelihood decoding in the high-rate, large-array regime.