The widespread movement to integrate computational thinking (CT) and computer science (CS) into K-12 education has led to rapid expansion. However, this growth is challenged by the reality that many in-service teachers asked to teach CS/CT are novices with little formal preparation. Professional development is critical to bridging this gap, yet many programs struggle to connect with teachers' diverse experiences and move beyond theory to classroom practice. This paper presents an experience report from a multi-year one-week summer course serving over 100 middle and high school teachers, the majority of whom were novices in CT. Our work contributes a novel pedagogical model for CT professional development grounded in the principles of Culturally Relevant Pedagogy (CRP). It is structured around two key components within each session: a ''Teachers as Learners'' mini-lecture that connects CT concepts to teachers' own lived experiences, followed immediately by a ''Teachers as Teachers'' hands-on exercise where they adapt K-12 CT activities for their own students. We call it the ''Dual CRP Model''. Pre- and post-course survey data consistently show that teachers enter with low confidence and leave feeling highly prepared to teach CT. Qualitative findings and an analysis of course artifacts reveal that the model's success stems from its use of culturally relevant analogies to make concepts accessible and its bridge from theory to classroom practice, which are key factors in building both conceptual understanding and pedagogical confidence.

This research to practice WIP paper describes capstone projects in software engineering which effectively combine theoretical education with practical skills, fostering career development in alignment with Career Construction Theory (CCT). This paper introduces a course designed to enhance students' career readiness by incorporating Agile methodologies for soft skills development, proficiency in modern technologies like Large Language Models (LLMs), and targeted career preparation such as resume building. The course's effectiveness, evaluated through CCT adaptability for 42 students, shows a positive impact on career preparedness in three of the four dimensions. This is the first attempt to measure the impact of a capstone course on career development using CCT adaptability. While the initial results are promising, further research is crucial to fully enhance all dimensions of CCT adaptability and to explore the scalability of this study across other engineering fields, potentially transforming engineering education and career preparation on a broader scale.

With the popularity of mobile devices with global positioning system (GPS), transportation network company (TNC) service has become an indispensable option of people's daily commute. However, it also provides opportunities for malicious parties to compromise TNC users’ location privacy. There are great challenges to preserve TNC users’ location privacy while improving the revenue of TNC and its quality of service (QoS). To address this issue, we propose a novel scheme to schedule the TNC vehicles while preserving the TNC users’ location differential privacy. Briefly, we add high dimensional Laplace noises to guarantee the TNC users’ geo-indistinguishability. Due to the differential private obfuscation, the demand for TNC vehicles in an area becomes uncertain. Thus, we employ the data-driven approach to characterize users’ demand uncertainty, formulate the TNC's revenue maximization problem into risk-averse stochastic programming, and provide corresponding feasible solutions. Using the released public data of Didi Chuxing, we conduct extensive simulations to evaluate the performance of the proposed scheduling scheme and compare the results under different$\zeta$-structure metrics. The results show that the proposed scheme can efficiently schedule the TNC vehicles, maximize the TNC's revenue and provide a better service for TNC users while protecting the TNC users’ location privacy.

Federated learning (FL) is a promising distributed learning paradigm, which can effectively avoid the privacy leakage and communication issues compared with the centralized learning. Specifically, in each training iteration, FL nodes only upload the local training results to the centralized server without disclosure of their raw training dataset and the centralized server will aggregate the local results of all FL nodes and update the global model. To this end, the performance of the global model is highly dependent on the nodes' cooperation. However, it is challenging to motivate mobile edge devices to involve themselves in the FL process without a desired incentive. Another significant concern of the mobile edge devices is the communication and computational energy cost of participation. Therefore, considering the high cost and weak communication channel with the centralized server specially for the distant nodes, in this paper, we propose a relay-assisted energy efficient scheme for federated learning, where each FL computational node is not only motivated by monetary awards based on their local dataset, but also further motivated to function as a relay node to assist distant nodes on local results uploading due to its locality advantage. To achieve a stable pairing solution between FL computational nodes and assisted relays in a distributive fashion, a many-to-one matching algorithm is applied, where each the computational node and relay is unable to deviate with current pairing unilaterally for higher revenue. Extensive simulations are conducted to illustrate the correctness and effectiveness of our proposed scheme.

Spectrum trading benefits both secondary users (SUs) and primary users (PUs), while it poses great challenges to maximize PUs' revenue, since SUs' demands are uncertain and individual SU's traffic portfolio contains private information. In this paper, we propose a data-driven spectrum trading scheme which maximizes PUs' revenue and preserves SUs' demand differential privacy. Briefly, we introduce a novel network architecture consisting of the primary service provider (PSP), the secondary service provider (SSP) and the secondary traffic estimator and database (STED). Under the proposed architecture, PSP aggregates available spectrum from PUs, and sells the spectrum to SSP at fixed wholesale price, directly to SUs at spot price, or both. The PSP has to accurately estimate SUs' demands. To estimate SUs' demand, the STED exploits data-driven approach to choose sampled SUs to construct the reference distribution of SUs' demands, and utilizes reference distribution to estimate the demand distribution of all SUs. Moreover, the STED adds noises to preserve the demand differential privacy of sampled SUs before it answers the demand estimation queries from the PSP. With the estimated SUs' demand, we formulate the revenue maximization problem into a risk-averse optimization, develop feasible solutions, and verify its effectiveness through both theoretical proof and simulations.

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.

The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local private data and model exchanges among agents impose critical privacy concerns. Despite some pioneering works to relieve such concerns, differentially private ADMM still confronts many research challenges. For example, the guarantee of differential privacy (DP) relies on the premise that the optimality of each local problem can be perfectly attained in each ADMM iteration, which may never happen in practice. The model trained by DP ADMM may have low prediction accuracy. In this paper, we address these concerns by proposing a novel (Improved) Plausible differentially Private ADMM algorithm, called PP-ADMM and IPP-ADMM. In PP-ADMM, each agent approximately solves a perturbed optimization problem that is formulated from its local private data in an iteration, and then perturbs the approximate solution with Gaussian noise to provide the DP guarantee. To further improve the model accuracy and convergence, an improved version IPP-ADMM adopts sparse vector technique (SVT) to determine if an agent should update its neighbors with the current perturbed solution. The agent calculates the difference of the current solution from that in the last iteration, and if the difference is larger than a threshold, it passes the solution to neighbors; or otherwise the solution will be discarded. Moreover, we propose to track the total privacy loss under the zero-concentrated DP (zCDP) and provide a generalization performance analysis. Experiments on real-world datasets demonstrate that under the same privacy guarantee, the proposed algorithms are superior to the state of the art in terms of model accuracy and convergence rate.

The development of 5G network and rapid growth of mobile traffic bring lucrative opportunities for Micro Operators (μOs), the novel local operators who own local spectrum, deploy and manage small cells (e.g., femtocells) in a specific area. Collaborating with traditional mobile network operators (MNOs), μOs gain profits from helping to offload the traffic carried by macro base stations and providing the MNOs customers with seamless service. However, due to the demand uncertainty and the sensitivity of individual user's demand profile, it is challenging for μOs to allocate the resource properly to avoid the under-and over-utilized situations. To address this issue, in this paper, we propose to employ data-driven approach to characterize the demand uncertainty, exploit differential privacy protocols to protect user's demand profile, and formulate the small cell planning problem into two-stage stochastic programming optimization with the objective of minimizing the capital and operational costs of μOs. Based on the formulated problem, we develop feasible solutions and conduct extensive simulations using real-world base station accessing real cellular data (i.e., data of 4G LTE network in Zhengzhou city, China) to verify the effectiveness of the proposed model.

The auction mechanism is deemed to be an effective method to address the problem of spectrum scarcity. Numerous spectrum auction mechanisms can alleviate spectrum shortage under the consideration of truthfulness, social welfare maximization and spectrum reusability, while the privacy preservation and preferences of primary/secondary users have not been fully discussed. In this paper, we propose a matching based double auction mechanism for spectrum trading with differential privacy (MastDP) to protect the privacy of buyers/sellers from the untrustworthy auctioneer, other buyers/sellers and other potential parties. Each participant adds distributed differential private noise following Geom(α) distribution to his bid value and encrypts the noisy bid value. The auctioneer can decrypt only the sum of all uploaded noisy bid values and determines the clearing price by using its private key. Based on the clearing price, the matching theory is adopted to maximize the winning participants' revenue while fully considering their preferences and spectrum reuse. Simulation results show that MastDP achieves satisfactory performance in terms of economic properties' privacy preservation and spectrum trading efficiency.

In the coming fifth generation (5G) or beyond 5G next generation networks (NGNs), the small cell deployment is a promising solution to meet the ever increasing demands of mobile devices, and the proliferation of wireless services. The low power base station (BS), such as femtocell BS, is a cost-effective and environmental friendly substitution for the power-hungry macrocell BS. One potentially effective way to deploy those small cells is to use two-tier NGN architecture, where the first-tier carrier can authorize the second-tier carrier's access to users' transmission information database (e.g., uplink/downlink service demands), and thereafter the second-tier carrier can decide how to place small cell BSs according to the mobile users' requirements locally. However, the second-tier carriers/operators for small cell placement may not be trustworthy, and the NGN users' data privacy might be compromised. To address this issue, we integrate differential privacy (DP) preserving techniques into data-driven optimization, and propose a novel scheme that not only preserves the privacy of NGN users' transmission information, but also maximizes the revenue of small cell deployment. Briefly, differential private noises are intentionally added into the users' transmission information database. Based on queries, the second-tier carrier can aggregate a given set of users' differentially private historical data, estimate the users' demands, and formulate the data-driven revenue maximization problem. Given the stochastic programming optimization formulation, we develop feasible solutions and conduct extensive simulations with real-world transmission datasets (i.e., transmission data collected hourly from 3072 4G eNBs deployed in several southern cities of China in 2015) to verify the effectiveness of the proposed scheme.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those ``espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

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.

Smart homes (SHs) aim at forming an energy optimized environment that can efficiently regulate the use of various Internet of Things (IoT) devices in its network. Real-time electricity pricing models along with SHs provide users an opportunity to reduce their electricity expenditure by responding to the pricing that varies with different times of the day, resulting in reducing the expenditure at both customers’ and utility provider’s end. However, responding to such prices and effectively scheduling the appliances under such complex dynamics is a challenging optimization problem to be solved by the provider or by third party services. As communication in SH-IoT environment is extremely sensitive and private, reporting of such usage information to the provider to solve the optimization has a potential risk that the provider or third party services may track users’ energy consumption profile which compromises users’ privacy. To address these issues, we developed a homomorphic encryption-based alternating direction method of multipliers approach to solve the cost-aware appliance scheduling optimization in a distributed manner and schedule home appliances without leaking users’ privacy. Through extensive simulation study considering real-world datasets, we show that the proposed secure appliance scheduling for flexible and efficient energy consumption scheme, namely SAFE, effectively lowers electricity cost while preserving users’ privacy.

Spectrum trading creates more accessing opportunities for secondary users (SUs), and economically benefits the primary users (PUs). Compared with centralized spectrum trading designs, e.g., spectrum auction, distributed spectrum trading captures instantaneous spectrum trading opportunities better over large geographical regions without incurring extra infrastructure deployment and has no network scalability issues. However, the existing distributed spectrum trading designs have limited concern regarding spectrum reuse. Considering spatial reuse, in this paper, we propose a novel distributed frequency reuse-based opportunistic spectrum trading (D-FROST) scheme, which can further improve spectrum utilization, provide more accessing opportunities for SUs, and increase the revenues of PUs. In this paper, we employ conflict graph to characterize the SUs' co-channel and radio interferences, and mathematically formulate a centralized PUs' revenue maximization problem under multiple wireless transmission constraints. Due to the NP-hardness to solve the problem and the non-existence of centralized trading entity, we develop the D-FROST algorithms based on matching with evolving preferences, and prove its stability. Through extensive simulations, we show that the proposed D-FROST algorithm is superior to other distributed spectrum trading algorithms without considering spectrum reuse, yields results close to the centralized optimal one, and is effective in increasing PUs' revenue and improving spectrum utilization.

Recently opened spectrum within 3550-3700 MHz provides more accessing opportunities to secondary users (SUs), while it also raises concerns on the operational privacy of primary users (PUs), especially for military and government. In this paper, we propose to study the tradeoff between PUs' temporal privacy and SUs' network performance using the data-driven approach. To preserve PUs' temporal operational privacy, we develop an obfuscation strategy for PUs, which allows PUs to intentionally add dummy signals to change the distribution of temporal spectrum availability, and confuse the adversary. While generating the dummy signals for privacy, the PUs have to consider the utility of SUs and try their best to satisfy SUs' uncertain traffic demands. Based on the historical data, we employ a data-driven risk-averse model to characterize the uncertainty of SUs' demands. With joint consideration of PUs' privacy and uncertain SUs' demands, we formulate the data-driven risk- averse stochastic optimization, and provide corresponding solutions. Through numerical simulations, we show that the proposed scheme is effective in preserving PUs' temporal operational privacy while offering good enough spectrum resources to satisfy SUs' traffic demands.

Spectrum trading benefits secondary users (SUs) with accessing opportunities and primary users (PUs) with monetary gains in cognitive radio networks. In the spectrum trading market, bidding privacy is a serious concern for SUs, since untrustworthy PUs or spectrum traders may leverage bidding information to manipulate the trading or leak SU's bids to other SU competitors for illegal gains. Current centralized privacy preserving spectrum trading designs may incur extra infrastructure deployment, miss many instantaneous spectrum accessing opportunities, and have scalability issues. In this paper, we propose a novel privacy-preserving semi-distributed spectrum trading scheme, which has joint consideration of spectrum reuse, SUs' bidding privacy preservation, and PUs' revenue maximization. We use the conflict graph to characterize SUs' interference relationships, and jointly employ the Paillier Cryptosystem and dynamic matching with evolving preferences to conduct the privacy-preserving spectrum trading in a semi-distributed manner. Through security, complexity and performance analysis, we show that the proposed scheme can effectively preserve the privacy of SUs' bidding values, and notably increase the PUs' revenue and improve spectrum utilization with limited overhead.

Spectrum trading not only improves spectrum utilization but also benefits both secondary users (SUs) with more accessing opportunities and primary users (PUs) with monetary gains. Although existing centralized designs consider the special features of spectrum trading (e.g., frequency reuse, interference mitigation, multi-radio multi- channel transmissions, etc.), they have to deploy new infrastructure, deal with extra control overhead, have scalability issues, and may miss many instantaneous opportunities. To address those issues, in this paper, we propose a novel dynamic matching based distributed spectrum trading (DMDST) scheme in multi-radio multi- channel cognitive radio (CR) networks. We employ conflict graph to characterize interference relationship among SUs with multiple CR radios, and formulate the centralized PUs' revenue maximization problem under multiple constrains. In view of the NP- hardness of solving the problem and no existence of centralized entity, we develop the DMDST algorithms based on conflict graph observed by PUs, solve the problem via dynamic matching with evolving preferences, and prove its stability. Through extensive simulations, we show that the results of proposed DMDST algorithm is close to the optimal one and outperforms other distributed algorithms without considering spectrum reuse.

Spectrum trading not only improves spectrum utilization but also benefits both secondary users (SUs) with more accessing opportunities and primary users (PUs) with monetary gains. Although the existing centralized designs consider the special features of spectrum trading (e.g., frequency reuse, interference mitigation, multi-radio multi-channel transmissions, and so on), they still have to face many practical but challenging issues, such as the new infrastructure deployment, the extra control overhead, and the scalability issues. To address those issues, in this paper, we propose a novel matching-based multi-radio multi-channel spectrum trading (M3-STEP) scheme in cognitive radio (CR) networks. We employ conflict graph to characterize the interference relationship among SUs with multiple CR radios, and formulate the centralized PUs' revenue maximization problem under multiple constrains. In view of the NP-hardness of solving the problem and no existence of centralized entity, we develop the M3-STEP algorithms based on conflict graph observed by PUs, solve the problem via dynamic matching with evolving preferences, and prove its pairwise stability. Simulation results show that the proposed M3-STEP algorithm achieves close to optimal performance and outperforms other distributed algorithms without considering spectrum reuse.