To address the issue of spatiotemporal coupling in random charging loads caused by the large-scale integration of electric vehicles, which disrupts the dynamic stability of microgrids, a Lyapunov-based microgrid optimization scheduling method (LOSM) is proposed. The key innovation lies in its dual virtual queue system, which meticulously models the spatiotemporally coupled randomness of BSS operation without relying on any forecasting, setting it apart from traditional prediction-dependent approaches. First, an energy storage system model incorporating a dynamic energy management mechanism is established. In contrast to traditional forecasting-dependent methods, our approach introduces a virtual queue for the battery unit state of charge (SOC), which enables real-time adaptability and significantly reduces reliance on prediction accuracy. This mechanism accurately characterizes the time-varying charging and discharging processes, thereby mitigating the impact of power fluctuations caused by charging on system frequency stability. Subsequently, a spatiotemporally correlated virtual queue is constructed for the multimodal operation of the battery swapping station, further enhancing real-time tracking capability without requiring high-accuracy forecasts. This achieves multidimensional and precise tracking of its electrical energy state. By integrating these virtual queues with a penalty factor adjustment mechanism, the proposed method simultaneously optimizes operational economy and dynamic stability while satisfying system stability constraints. Simulation analysis under a typical diurnal residential load profile with a 50-EV battery swapping station and a significant penetration of wind and photovoltaic generation demonstrates that the proposed approach increases the revenue of the battery swapping station by 5.67% and reduces the start-stop frequency of the diesel generator by 37.04%. This study provides a theoretical foundation for the robust operation of microgrids in scenarios involving electric vehicle integration and serves as an important reference for establishing dynamic stability assurance mechanisms in new power systems.

Information-centric networking (ICN) as an emerging networking paradigm has recently gained significant attention, due to the improvement of content delivery efficiency. The built-in network storage for caching is a key component in ICN to provide low latency service and reduce high backhaul traffic by caching popular content. However, users' content preference contains individual sensitive characteristics which is distinguishable from others. Therefore, in this work, we propose a data-driven caching revenue maximization problem with the considerations of users' local differential privacy. Specifically, we employ dBitFlip, a local differential privacy (LDP) mechanism, to locally add differential private noise to the users' preference content information. We leverage data-driven approach to predict the content popularity based on the reference distribution constructed by the reported noisy preference content data from users, mathematically present the distance between the noisy reference distribution and the true distribution by the tolerance level, and prove the relationship among the tolerance level, differential privacy budget and the confidence level. We provide feasible solutions to the proposed revenue maximization problem, and conduct simulations to show the effectiveness of the proposed scheme.

Machine learning has a vast outreach in principal applications and uses large amount of data to train the models, prompting a viable and easy to use Machine Learning as a Service (MLaaS). This flexible paradigm however, could have immense privacy implications since the training data often contains sensitive features, and adversarial access to such models could pose a security risk. In adversarial attacks such as model inversion attack on a system used for face recognition, an adversary uses the output (target label) to reconstruct the input (image of the target individual from the training dataset). To avert such a vulnerability of the system, in this paper, we develop a novel approach of applying perceptual hash to parts of the given training images that leverages the functional mechanism of image hashing. The facial recognition system is then trained over this newly created dataset of perceptually hashed images and high classification accuracy is observed. Furthermore, we demonstrate a series of model inversion attacks emulating adversarial access that yield hashed images of target individuals instead of the original training dataset images; thereby preventing original image reconstruction and counteracting the inversion attack. Through rigorous empirical evaluations of applying the proposed formulation over real world dataset, we verify the effectiveness of our proposed framework in protecting the training image dataset and counteracting inversion attack.

To provide intelligent and personalized services on smart devices, machine learning techniques have been widely used to learn from data, identify patterns, and make automated decisions. Machine learning processes typically require a large amount of representative data that are often collected through crowdsourcing from end users. However, user data could be sensitive in nature, and learning machine learning models on these data may expose sensitive information of users, violating their privacy. Moreover, to meet the increasing demand of personalized services, these learned models should capture their individual characteristics. This paper proposes a privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data. Practical issues in a distributed learning system such as user heterogeneity are considered in the proposed approach. Moreover, the convergence property and privacy guarantee of the proposed approach are rigorously analyzed. Experiments on realistic mobile sensing data demonstrate that the proposed approach is robust to high user heterogeneity and offer a trade-off between accuracy and privacy.

To provide intelligent and personalized services on smart devices, machine learning techniques have been widely used to learn from data, identify patterns, and make automated decisions. Machine learning processes typically require a large amount of representative data that are often collected through crowdsourcing from end users. However, user data could be sensitive in nature, and training machine learning models on these data may expose sensitive information of users, violating their privacy. Moreover, to meet the increasing demand of personalized services, these learned models should capture their individual characteristics. This article proposes a privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data. Practical issues in a distributed learning system such as user heterogeneity are considered in the proposed approach. In addition, the convergence property and privacy guarantee of the proposed approach are rigorously analyzed. The experimental results on realistic mobile sensing data demonstrate that the proposed approach is robust to user heterogeneity and offers a good tradeoff between accuracy and privacy.

Data centers are key participants in emergency demand response (EDR), where the grid coordinates large electricity consumers for reducing their consumption during emergency situations to prevent major economic losses. While existing literature concentrates on owner-operated data centers (e.g., Google), this work studies EDR in multi-tenant colocation data centers (e.g., Equinix) where servers are owned and managed by individual tenants and which are better targets of EDR. Existing EDR mechanisms incentivize tenants energy reduction. Such designs can either be gamed by strategic tenants or untrustworthy colocation operators for illegal gains. These serious privacy concerns stand as barrier preventing the tenants' participation in EDR. This paper addresses such concerns by proposing a privacy-preserving and strategy-proof mechanism using the descending clock auction. Privacy is protected by implementing homomorphic encryption for aggregation through the clock auction, where operator can only know the aggregate of the tenants' values or bids but not their individual private values or confidential information submitted to meet the EDR. We evaluate the privacy and performance of this scheme by formulating descending clock auction, in which the amount of energy/price the tenants are willing to reduce for a given price/energy to meet EDR is protected.

Information-centric networking (ICN) is developed for the future Internet because of the tremendous increase of content demands in the Internet. In the ICN architecture, in-network storage for caching plays an important role in improving content delivery efficiency, scalability and availability. To enjoy the benefits of caching users' preferable contents without disclosing the users' privacy, in this paper, we aim to integrate local differential privacy (LDP) techniques into data-driven optimization, and propose a novel scheme to allow content provider (CP) to collect the locally differentially private content preferences of a selected group of users, exploit data-driven approach to predict the content popularity, and offer the cache-enabled access points (APs) economic incentives to cache the selected preferable content. Here, optimized local hashing (OLH) is employed to locally add differential private noise to the users' preference content information and the noisy data is sent to the CP. Besides, we leverage data-driven methodology to predict the content popularity according to the constructed reference distribution of the given noisy preference content data from users. We formulate a data-driven caching revenue optimization, provide feasible solutions, and conduct simulations to show the effectiveness of the proposed scheme.

Radio-Frequency Identification (RFID) nowadays has been widely used in a variety of fields such as manufacturing, transportation and healthcare, for identification, tracking, and information collection due to the simplicity and low cost of RFID tags. Existing RFID information collection protocols focus on information collection at a single reader, which aim to minimize the collision among RFID tags and the execution time for information collection. However, none of them consider reader scheduling to deal with the collision between neighboring readers that exists in multi-reader RFID systems. In this paper, we propose two reader scheduling schemes for time-efficient information collection in multi-reader RFID systems. The two proposed scheduling schemes can coordinate multiple readers to collect information simultaneously and ensure information from all tags is successfully collected with reader-reader collision. Extensive simulation results demonstrate the efficiency of the proposed reader scheduling schemes for multi-reader information collection.

Data centers are recognized as promising resources for emergency demand response (EDR) that requires a certain amount of power reduction when system reliability is in danger. In this paper, we study EDR in a colocation data center where multiple tenants deploy their own servers in a shared space managed by a data center operator. While the data center operator desires to reduce the usage of expensive and environmentally unfriendly backup generation during EDR events, the tenants who can control their servers have little incentive to reduce their power consumption. To enable cost-effective and eco-friendly EDR, the data center operator has to properly incentivize the tenants to modulate server power consumption. Furthermore, the social welfare generated during EDR should be properly shared among the data center operator and tenants so that all of them are satisfied. We propose an approach based on the Nash bargaining solution, which is Pareto efficient, fair and social welfare maximizing, to incentivize the tenants' participation and allocate the social welfare among the data center operator and tenants properly. Trace-driven simulations are conducted to demonstrate the effectiveness of our proposed approach.

Recent years witness the rapid development of cloud computing and more and more data owners outsource their data to the cloud. To eliminate the disclosure of authorized data users' privacy in cloud services (e.g., cloud storage or cloud-assisted computing)-since the cloud providers cannot be fully trustworthy-several previous works have proposed considerable privacy-preserving schemes by exploiting searchable encryption. However, owing to its feature of untrusty, issue arises on how data users can verify whether the cloud has faithfully executed the search operations or not. Motivated by this question, in this paper, we propose a searchable and verifiable query scheme over encrypted data based on Counting Bloom Filter (CBF). Specifically, we deploy counting bloom filters to generate proofs for data users' queries in the private cloud; using the consistence between algebra operations on counting bloom filters and on data sets, we can verify the integrity of search result. The security analysis and performance evaluation show that the proposed scheme is privacy-preserving and is feasible to implement.

Cognitive Radio (CR) technique is proved to be an efficient approach for mitigating the spectrum scarcity problem in wireless communications and spectrum allocation methods construct the foundation of such a technique. However, it will degrade the performance of CR when paying no attention to the behavior of second users (SUs) and their demands for spectrum attribute. In this paper, the spectrum allocation problem is first modeled as a coalition formation game, taking SUs reputation and requirements of spectrums into consideration. The rule named Assigning spectrum by probability among coalitions and by demand within coalitions is used to optimize the spectrum utilization and fairness when allocating spectrum among SUs. For each SU is equipped with an agent, a central authority is not needed and therefore the scheme can be more applicable in dynamic cognitive radio networks. Simulation results show that the proposed approach can efficiently improve the fairness and efficiency of spectrum allocation in CR context.

In recent years, Cognitive Radio (CR) has been regarded as the most promising technique for solving the problem of spectrum utilization. CR can scan the spectrum band and identify free channels which will be used by cognitive users with no harm to primary users. Due to the various dynamic characteristics of Cognitive Radio Networks (CRN), adaptation and secure communication are of greater significance than other wireless networks. In this paper, we propose an adaptive trust management mechanism based on cognition cycle, which can mitigate system error rate effectively and make spectrum allocation more reasonable and secure. Finally, simulation results show that the scheme has the advantages of strong adaptation and low error rate.