Trade Privacy for Utility: A Learning-Based Privacy Pricing Game in Federated Learning

To prevent implicit privacy disclosure in sharing gradients among data owners (DOs) under federated learning (FL), differential privacy (DP) and its variants have become a common practice to offer formal privacy guarantees with low overheads. However, individual DOs generally tend to inject larger DP noises for stronger privacy provisions (which entails severe degradation of model utility), while the curator (i.e., aggregation server) aims to minimize the overall effect of added random noises for satisfactory model performance. To address this conflicting goal, we propose a novel dynamic privacy pricing (DyPP) game which allows DOs to sell individual privacy (by lowering the scale of locally added DP noise) for differentiated economic compensations (offered by the curator), thereby enhancing FL model utility. Considering multi-dimensional information asymmetry among players (e.g., DO's data distribution and privacy preference, and curator's maximum affordable payment) as well as their varying private information in distinct FL tasks, it is hard to directly attain the Nash equilibrium of the mixed-strategy DyPP game. Alternatively, we devise a fast reinforcement learning algorithm with two layers to quickly learn the optimal mixed noise-saving strategy of DOs and the optimal mixed pricing strategy of the curator without prior knowledge of players' private information. Experiments on real datasets validate the feasibility and effectiveness of the proposed scheme in terms of faster convergence speed and enhanced FL model utility with lower payment costs.Comment: Accepted by IEEE ICC202

Organic carbon deposition flux on the North Chukchi Sea shelf based on 210Pb radioactivity dating

Deposition of organic carbon forms the final net effect of the ocean carbon sink at a certain time scale. Organic carbon deposition on the Arctic shelves plays a particularly important role in the global carbon cycle because of the broad shelf area and rich nutrient concentration. To determine the organic carbon deposition flux at the northern margin of the Chukchi Sea shelf, the 210Pb dating method was used to analyze the age and deposition rate of sediment samples from station R17 of the third Chinese National Arctic Research Expedition. The results showed that the deposition rate was 0.6 mm∙a-1, the apparent deposition mass flux was 0.72 kg∙m-2∙a-1, and the organic carbon deposition flux was 517 mmol C∙m-2∙a-1. It was estimated that at least 16% of the export organic carbon flux out of the euphotic zone was transferred and chronically buried into the sediment, a value which was much higher than the average ratio (~10%) for low- to mid-latitude regions, indicating a highly effective carbon sink at the northern margin of the Chukchi Sea shelf. With the decrease of sea ice coverage caused by warming in the Arctic Ocean, it could be inferred that the Arctic shelves will play an increasingly important role in the global carbon cycle

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

Secrecy Outage Probability Fairness for Intelligent Reflecting Surface-Assisted Uplink Channel

This paper investigates physical layer security (PLS) in the intelligent reflecting surface (IRS)-assisted multiple-user uplink channel. Since the instantaneous eavesdropper's channel state information (CSI) is unavailable, the secrecy rate can not be measured. In this case, existing investigations usually focus on the maximization of the minimum (max-min) of signal to interference plus noise power ratio (SINRs) among multiple users, and do not consider secrecy outage probability caused by eavesdroppers. In this paper, we first formulate the minimization of the maximum (min-max) secrecy outage probability among multiple users. The formulated problem is solved by alternately optimizing receiving matrix and phase shift matrix. Simulations demonstrate that the maximum secrecy outage probability is significantly reduced with the proposed algorithm compared to max-min SINR strategies, meaning our scheme has a higher security performance

Research Progress on Controlled Low-Strength Materials: Metallurgical Waste Slag as Cementitious Materials

Increasing global cement and steel consumption means that a significant amount of greenhouse gases and metallurgical wastes are discharged every year. Using metallurgical waste as supplementary cementitious materials (SCMs) shows promise as a strategy for reducing greenhouse gas emissions by reducing cement production. This strategy also contributes to the utilization and management of waste resources. Controlled low-strength materials (CLSMs) are a type of backfill material consisting of industrial by-products that do not meet specification requirements. The preparation of CLSMs using metallurgical waste slag as the auxiliary cementing material instead of cement itself is a key feature of the sustainable development of the construction industry. Therefore, this paper reviews the recent research progress on the use of metallurgical waste residues (including blast furnace slag, steel slag, red mud, and copper slag) as SCMs to partially replace cement, as well as the use of alkali-activated metallurgical waste residues as cementitious materials to completely replace cement for the production of CLSMs. The general background information, mechanical features, and properties of pozzolanic metallurgical slag are introduced, and the relationship and mechanism of metallurgical slag on the performance and mechanical properties of CLSMs are analyzed. The analysis and observations in this article offer a new resource for SCM development, describe a basis for using metallurgical waste slag as a cementitious material for CLSM preparation, and offer a strategy for reducing the environmental problems associated with the treatment of metallurgical waste

A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification

In recent years, social network sentiment classification has been extensively researched and applied in various fields, such as opinion monitoring, market analysis, and commodity feedback. The ensemble approach has achieved remarkable results in sentiment classification tasks due to its superior performance. The primary reason behind the success of ensemble methods is the enhanced diversity of the base classifiers. The boosting method employs a sequential ensemble structure to construct diverse data while also utilizing erroneous data by assigning higher weights to misclassified samples in the next training round. However, this method tends to use a sequential ensemble structure, resulting in a long computation time. Conversely, the voting method employs a concurrent ensemble structure to reduce computation time but neglects the utilization of erroneous data. To address this issue, this study combines the advantages of voting and boosting methods and proposes a new two-stage voting boosting (2SVB) concurrent ensemble learning method for social network sentiment classification. This novel method not only establishes a concurrent ensemble framework to decrease computation time but also optimizes the utilization of erroneous data and enhances ensemble performance. To optimize the utilization of erroneous data, a two-stage training approach is implemented. Stage-1 training is performed on the datasets by employing a 3-fold cross-segmentation approach. Stage-2 training is carried out on datasets that have been augmented with the erroneous data predicted by stage 1. To augment the diversity of base classifiers, the training stage employs five pre-trained deep learning (PDL) models with heterogeneous pre-training frameworks as base classifiers. To reduce the computation time, a two-stage concurrent ensemble framework was established. The experimental results demonstrate that the proposed method achieves an F1 score of 0.8942 on the coronavirus tweet sentiment dataset, surpassing other comparable ensemble methods

A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects

By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of digital twins (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws and privacy invasions of IoDT hinders its wide deployment. Besides, the intrinsic characteristics of IoDT such as decentralized topology, information-centric routing and semantic communications entail critical challenges for security service provisioning in IoDT. To this end, this paper presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber-physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.</p