Privacy Protection Capability Research Articles

Research on Blockchain Interactive Zero Knowledge Proof Privacy Protection Scheme Based on Improved Paillier Homomorphic Encryption

In the context of the digital age, data privacy and security issues are increasingly prominent. Blockchain technology plays an important role in data sharing due to its transparency and immutability, but it also brings the risk of privacy leakage. Zero knowledge proof technology provides a solution for verifying data correctness without exposing data content, which is particularly important for blockchain as it can ensure the validity and compliance of transactions while protecting user privacy. Although zero knowledge proof is quite mature in theory, its application in blockchain systems still faces challenges such as computational efficiency, complexity of smart contracts, and system compatibility. This study aims to propose a privacy protection scheme that supports interactive zero knowledge proof by improving the homomorphic encryption Paillier algorithm, in order to enhance the privacy protection capability of blockchain systems and maintain system efficiency and security. The study will adopt an interdisciplinary approach, combining cryptography, computer science, and network security theory, to deeply analyze the application effect of zero knowledge proof technology in blockchain, explore its optimization space and applicability.

Test time adaptation for cross-domain sleep stage classification

Domain shift is a serious problem with existing cross-domain sleep stage classification methods. However, most existing domain adaptation methods require access to a large amount of source domain data containing patient information, which may lead to infringement of patient privacy. At the same time, existing methods require collecting all information in advance when processing target data, which cannot meet the situation where test data arrives in batches or in order in actual scenarios. To this end, we propose a test time adaptation framework based on knowledge distillation for cross-domain sleep stage classification. This teacher-student network framework uses pre-trained model to learn variant features and robust representations. We evaluate our framework in the cross domain scenario composed of three datasets and validate its superiority, real time performance and privacy protection capability.

Toward a Secure and Private Cross-Chain Protocol Based on Encrypted Communication

Blockchain technology is becoming more prominent and is being used in many different industries. Data islands have emerged as a result of the difficulty in transferring assets and exchanging information between blockchains because of differences in the underlying technology. Cross-chain technology is becoming increasingly prevalent as a solution to the data security problem. Decentralized blockchain networks frequently use the Hashed Timelock Contract (HTLC) to solve the problem of balancing atomicity and time sensitivity. However, it suffers from drawbacks such as limited security and privacy protection capabilities. To overcome these limitations, a secure and fully functional system named the Exchange Smart Contract (ExchangeSC) has been developed; the ExchangeSC can integrate smart contracts and Paillier homomorphic encryption into the Mid-Account HTLC (MA-HTLC) cross-chain protocol. This integration effectively resolves the problem of low security and privacy protection in the HTLC cross-chain protocol. Specifically, the locked information in the solution is encrypted using homomorphic encryption before uploading to the blockchain, which is operated by participating nodes in the ciphertext domain. The ExchangeSC demonstrates reasonable performance on the official testing network’s EVM platform. Further evaluation of the ExchangeSC-based HTLC cross-chain reveals its superior security and lower time cost compared to the BitXHub cross-chain project.

TPE-BFL: Training Parameter Encryption scheme for Blockchain based Federated Learning system

Blockchain technology plays a pivotal role in addressing the single point of failure issues in federated learning systems, due to the immutable nature and decentralized architecture. However, traditional blockchain-based federated learning systems still face privacy and security challenges when transmitting training model parameters to individual nodes. Malicious nodes within the system can exploit this process to steal parameters and extract sensitive information, leading to data leakage. To address this problem, we propose a Training Parameter Encryption scheme for Blockchain based Federated Learning system (TPE-BFL). In TPE-BFL, the training parameters of the system model are encrypted using the paillier algorithm with the property of addition homomorphism. This encryption mechanism is integrated into the workflows of three distinct roles within the system: workers, validators, and miners. (1) Workers utilize the paillier encryption algorithm to encrypt training parameters for local training models. (2) Validators decrypt received encrypted training parameters using private keys to verify their validity. (3) Miners receive cryptographic training parameters from validators, validate them, and generate blocks for subsequent global model updates. By implementing the TPE-BFL mechanism, we not only preserve the immutability and decentralization advantages of blockchain technology but also significantly enhance the privacy protection capabilities during data transmission in federated learning systems. In order to verify the security of TPE-BFL, we leverage the semantic security inherent in the Paillier encryption algorithm to theoretically substantiate the security of our system. In addition, we conducted a large number of experiments on real-world data to prove the validity of our proposed TPE-BFL, and when 15% of malicious devices are present, TPE-BFL achieve 92% model accuracy, a 5% improvement over the blockchain-based decentralized FL framework (VBFL).

Private and utility enhanced intrusion detection based on attack behavior analysis with local differential privacy on IoV

In recent years, with the escalating security demands of the Internet of Vehicles (IoV), concerns over safety have intensified. To prevent security incidents and privacy breaches, IoV must address various threats promptly and effectively. The use of deep learning methods for intrusion detection in IoV has garnered widespread attention. Compared to traditional security defenses, deep learning can learn from heterogeneous data sources, enhancing the accuracy of detecting various security threats. However, current research based on deep learning primarily focuses on constructing intrusion detection models and overlooks the analysis and processing of extensive behavioral data. Moreover, model training requires access to and transmission of sensitive data, which may lead to high communication costs and potential privacy leaks. To ensure the network security of IoV, we propose FDL-IDM, an innovative behavior-analysis-based intrusion detection model leveraging differential privacy within federated learning. It extracts driving behavior spatiotemporally and employs noise perturbation pre-aggregation, reducing communication costs and ensuring privacy without compromising accuracy. Specifically, we process data from both temporal and spatial dimensions. Data are grouped based on sender identity and then sliced according to the time sequence to create state matrices that vary over time, enhancing the performance and robustness of the detection model. Next, we incorporate an attention mechanism to merge outputs from each time step and hidden layer, strengthening the time series model and reducing information loss. Lastly, in federated learning, we add noise perturbation to the uploaded parameters, reducing the risk of privacy breaches. Additionally, we employ a random scheduling strategy during training to select clients and assign an adjusted learning rate that decreases with iterations, enhancing the stability of model training. Therefore, FDL-IDM helps prevent security attacks and protect IoV privacy. Through experiments and privacy analysis, as well as tests on vehicle-level devices, FDL-IDM achieved F1-scores of 0.9751, 0.9851, and 0.9789 on three public datasets, demonstrating not only high accuracy but also robust privacy protection capabilities.

Quantum differential privacy under noise channels

Quantum differential privacy is a way to protect privacy in quantum computing. It perturbs the final state of quantum computing by a specific quantum noise channel and makes the output useless to the attackers, so as to achieve privacy protection. In this paper, we study the quantum differential privacy under three channels: bit flip channel, phase flip channel and depolarizing channel. We calculate the privacy parameters for these three channels based on the proportional distance. We show the privacy protection capability by calculating the decay rates of the trace distances of two arbitrary quantum states before and after passing through the channels. Under the same privacy budget, we compare the fidelities, the trace distances between the real outputs and the outputs after entering the channels, and analyze the privacy protection performance of different quantum channels in detail.

A Novel Federated Learning Framework Based on Conditional Generative Adversarial Networks for Privacy Preserving in 6G

With the rapid development of 6G networks, data transmission speed has significantly increased, making data privacy protection issues even more crucial. The federated learning (FL) is a distributed machine learning framework with privacy protection and secure encryption technology, aimed at enabling dispersed participants to collaborate on model training without disclosing private data to other participants. Nonetheless, recent research indicates that the exchange of shared gradients may lead to information disclosure, and thus FL still needs to address privacy concerns. Additionally, FL relies on a large number of diverse training data to forge efficient models, but in reality, the training data available to clients are limited, and data imbalance issues lead to over fitting in existing federated learning models. To alleviate these issues, we introduce a Novel Federated Learning Framework based on Conditional Generative Adversarial Networks (NFL-CGAN). NFL-CGAN divides the local networks of each client into private and public modules. The private module contains an extractor and a discriminator to protect privacy by retaining them locally. Conversely, the public module is shared with the server to aggregate the shared knowledge of clients, thereby improving the performance of each client local network. Comprehensive experimental analyses demonstrate that NFL-CGAN surpasses traditional FL baseline methods in data classification, showcasing its superior efficacy. Moreover, privacy assessments also verified robust and reliable privacy protection capabilities of NFL-CGAN.

Dual blockchain-based data sharing mechanism with privacy protection for medical internet of things

In the period of big data, the Medical Internet of Things (MIoT) serves as a critical technology for modern medical data collection. Through medical devices and sensors, it enables real-time collection of a large amount of patients' physiological parameters and health data. However, these data are often generated in a high-speed, large-scale, and diverse manner, requiring integration with traditional medical systems, which further exacerbates the phenomenon of scattered and heterogeneous medical data. Additionally, the privacy and security requirements for the devices and sensor data involved in the MIoT are more stringent. Therefore, when designing a medical data sharing mechanism, the data privacy protection capability of the mechanism must be fully considered. This paper proposes an alliance chain medical data sharing mechanism based on a dual-chain structure to achieve secure sharing of medical data among entities such as medical institutions, research institutions, and cloud privacy centers, and at the same time provide privacy protection functions to achieve a balanced combination of privacy protection capability and data accessibility of medical data. First, a knowledge technology based on ciphertext policy attribute encryption with zero-knowledge concise non-interactive argumentation is used, combined with the data sharing structure of the federation chain, to ensure the integrity and privacy-protecting capability of medical data. Second, the approach employs certificate-based signing and proxy re-encryption technology, ensuring that entities can decrypt and verify medical data at the cloud privacy center using this methodology, consequently addressing the confidentiality concerns surrounding medical data. Third, an efficient and secure key identity-based encryption protocol is used to ensure the legitimacy of user identity and improve the security of medical data. Finally, the theoretical and practical performance analysis proves that the mechanism is feasible and efficient compared with other existing mechanisms.

HN3S: A Federated AutoEncoder framework for Collaborative Filtering via Hybrid Negative Sampling and Secret Sharing

Federated recommender systems can serve users with suitable item recommendations while preserving their privacy, but most current works cannot serve non-participant users. We consider that Federated AutoEncoder for Collaborative Filtering (FAE-CF) is expected to solve this problem. However, FAE-CF encounters privacy protection concerns and high communication overhead issues. In this paper, we investigate FAE-CF’s privacy protection capabilities and communication volume, unveiling its potential privacy security issues and inferior practicality. By analyzing the FAE-CF gradient, we propose an effective attack that could recover most user data, with all recovery metrics above 95.53% in our simulation experiments. To address these challenges, we propose Hybrid Negative Sampling and Secret Sharing (HN3S), a privacy-preserving method designed for FAE-CF. HN3S uses hybrid negative sampling to reduce communication overhead and secret sharing to protect user data. Experiments conducted on various public datasets with multiple AE-CFs have shown that HN3S can effectively protect the privacy of FAE-CF, reducing the communication overhead by up to 60.44%, while maintaining the performance reduction within 10.78%. Our research reveals significant privacy risks and poor utility inherent in current FAE-CFs, and provides a feasible solution to protect user privacy and balance communication overhead with algorithm performance.

GLPS: A Geohash-Based Location Privacy Protection Scheme.

With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named GLPS. On the user side, GLPS uses Geohash encoding technology to convert the user's location coordinates into a string code representing a rectangular geographic area. GLPS uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user's exact location. On the anonymous server side, the scheme takes advantage of Geohash codes' geospatial gridding capabilities and GL-Tree's effective location retrieval performance to generate a k-anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user's location. We experimentally tested the performance of GLPS and compared it with three schemes: Casper, GCasper, and DLS. The experimental results and analyses demonstrate that GLPS has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association.

RDP-GAN: A Rényi-Differential Privacy Based Generative Adversarial Network

Generative adversarial networks (GANs) have attracted increasing attention recently owing to their impressive abilities to generate realistic samples with high privacy protection. Without directly interacting with training examples, the generative model can be used to estimate the underlying distribution of an original dataset while the discriminator can examine model quality of the generated samples by comparing the label values with training examples. In considering privacy issues in GANS, existing works focus on perturbing the parameters and analyzing the corresponding privacy protection capability, and the parameters are not directly exchanged between the generator and discriminator in GANs. Thus, in this work, we propose a Rényi-differentially private-GAN (RDP-GAN), which achieves differential privacy (DP) in a GAN by carefully adding random Gaussian noise to the value of the exchanged loss function during training. Moreover, we derive analytical results characterizing the total privacy loss under the subsampling method and cumulative iterations, which show its effectiveness for the privacy budget allocation. In addition, in order to mitigate the negative impact of injecting noises, we enhance the proposed algorithm by adding an adaptive noise tuning step, which will change the amount of added noise according to the testing accuracy. Through extensive experimental results, we verify that the proposed algorithm can achieve a better privacy level while producing high-quality samples compared with a benchmark DP-GAN scheme based on noise perturbation on training gradients.

A Triple Real-Time Trajectory Privacy Protection Mechanism Based on Edge Computing and Blockchain in Mobile Crowdsourcing

With the rapid development of the Internet of Things (IoT) and the rapid popularization of 5G networks, the data that needs to be processed in Mobile Crowdsourcing (MCS) system is increasing every day. Traditional cloud computing can no longer meet the needs of crowdsourcing for real-time data and processing efficiency, thus, edge computing was born. Edge computing can be calculated at the edge of network so that greatly improve the efficiency and real-time performance of data processing. In addition, most of the existing privacy protection technologies are based on the trusted third parties. Therefore, in view of the semi-trustworthiness of edge servers and the transparency of blockchain, this paper proposes a triple real-time trajectory privacy protection mechanism (T-LGEB) based on edge computing and blockchain. Through combining the localized differential privacy and multiple probability extension mechanism, the T-LGEB mechanism is proposed to send the requests and data to the edge server in this paper. Then, through the spatio-temporal dynamic pseudonym mechanism proposed in the paper, the entire trajectory of task participants is divided into multiple unrelated trajectory segments with different pseudonymous identities in order to protect the trajectory privacy of task participants while ensuring high data availability and real-time data. Through a large number of experiments and comparative analysis on multiple real data sets, the proposed T-LGEB has extremely high privacy protection capabilities and data availability, and the resource consumption caused is relatively low.

Cross-domain privacy-preserving broad network for fault diagnosis of rotating machinery

The broad learning system-based data processing method has been widely used in the field of industrial intelligent operation and maintenance, and has achieved impressive results. However, data island created by privacy concerns in real industrial scenarios makes centralized deep training extremely difficult. In addition, the fluctuating operating conditions of machines including speed and load significantly affect the distribution of monitoring data representing the operating state of the device. These two problems make it difficult to apply broad learning system to practical application scenarios. Therefore, in view of the above problems, a novel cross-domain privacy-preserving broad network (CDPPBN) for cross-domain fault classification of rotating machinery is presented. Specifically, a cross-domain interactive data encryption protocol feature extractor is designed firstly to ensure the privacy and security of both source and target domain data during feature extraction process. Then, a domain adaptation target classifier is constructed to achieve the fault classification task in the target domain without labels. In this classifier, a maximum mean discrepancy regularization term is constructed to match the mean projections of the source and target domains to adapt the source classifier to the target domain, and a semi-supervised local graph embedding regularization term is constructed to mine the structural features of the target domain data. CDPPBN not only has high performance in terms of data privacy protection, but also makes full use of both labeled source data and unlabeled target data to generate a domain adaptation target classifier. The effectiveness of the presented method is verified through bearing monitoring signal. Experimental analysis shows that the proposed CDPPBN has better data privacy protection capability and data cross-domain diagnosis ability compared with other methods.

Are digital natives overconfident in their privacy literacy? Discrepancy between self-assessed and actual privacy literacy, and their impacts on privacy protection behavior.

Privacy literacy is recognized as a crucial skill for safeguarding personal privacy online. However, self-assessed privacy literacy often diverges from actual literacy, revealing the presence of cognitive biases. The protection motivation theory (PMT) is widely used to explain privacy protection behavior, positing that whether individuals take defensive measures depends on their cognitive evaluation of threats and coping capabilities. However, the role of cognitive biases in this process has been understudied in previous research. This study focuses on Chinese digital natives and examines the differential impacts of subjective and objective privacy literacy on privacy protection behavior, as well as the role of cognitive biases in privacy decision-making. The results show that there is no significant correlation between subjective and objective privacy literacy, and a bias exists. When privacy concern is used as a mediating variable, there are significant differences in the paths through which subjective and objective privacy literacy influence privacy protection behavior. Furthermore, privacy literacy overconfidence moderates the relationship between privacy concern and privacy protection behavior. The findings confirm the influence of cognitive biases in privacy behavior decision-making and extend the PMT. This study also calls for the government to enhance privacy literacy training for digital natives to improve their privacy protection capabilities.

Federated Learning-Based Insulator Fault Detection for Data Privacy Preserving.

Insulators are widely used in distribution network transmission lines and serve as critical components of the distribution network. The detection of insulator faults is essential to ensure the safe and stable operation of the distribution network. Traditional insulator detection methods often rely on manual identification, which is time-consuming, labor-intensive, and inaccurate. The use of vision sensors for object detection is an efficient and accurate detection method that requires minimal human intervention. Currently, there is a considerable amount of research on the application of vision sensors for insulator fault recognition in object detection. However, centralized object detection requires uploading data collected from various substations through vision sensors to a computing center, which may raise data privacy concerns and increase uncertainty and operational risks in the distribution network. Therefore, this paper proposes a privacy-preserving insulator detection method based on federated learning. An insulator fault detection dataset is constructed, and Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models are trained within the federated learning framework for insulator fault detection. Most of the existing insulator anomaly detection methods use a centralized model training method, which has the advantage of achieving a target detection accuracy of over 90%, but the disadvantage is that the training process is prone to privacy leakage and lacks privacy protection capability. Compared with the existing insulator target detection methods, the proposed method can also achieve an insulator anomaly detection accuracy of more than 90% and provide effective privacy protection. Through experiments, we demonstrate the applicability of the federated learning framework for insulator fault detection and its ability to protect data privacy while ensuring test accuracy.

Heterogeneous graph neural network with semantic-aware differential privacy guarantees

Most social networks can be modeled as heterogeneous graphs. Recently, advanced graph learning methods exploit the rich node properties and topological relationships for downstream tasks. That means that more private information is embedded in the representation. However, the existing privacy-preserving methods only focus on protecting the single type of node attributes or relationships, which neglect the significance of high-order semantic information. To address this issue, we propose a novel Heterogeneous graph neural network with Semantic-aware Differential privacy Guarantees named HeteSDG, which provides a double privacy guarantee and performance trade-off in terms of both graph features and topology. In particular, we first reveal the privacy leakage in heterogeneous graphs and define a membership inference attack with a semantic enhancement (MIS) that will improve the means of member inference attacks by obtaining side background knowledge through semantics. Then we design a two-stage mechanism, which includes the feature attention personalized mechanism and the topology gradient perturbation mechanism, where the privacy-preserving technologies are based on differential privacy. These mechanisms will defend against MIS and provide stronger interpretation, but simultaneously bring in noise for representation learning. To better balance the noise perturbation and learning performance, we utilize a bi-level optimization pattern to allocate a suitable privacy budget for the above two modules. Our experiments on four public benchmarks conduct performance experiments, ablation studies, inference attack verification, etc. The results show the privacy protection capability and generalization of HeteSDG.

SaNDA: A small and iNcomplete dataset analyser

In personalised health, small datasets with missing data are quite common. Current Machine Learning methods are unable to process such datasets in a meaningful way due to the huge data volume requirement. To address this problem, we propose a new Small and iNcomplete Dataset Analyser (SaNDA) to process such datasets in a meaningful way. Due to the characteristics of these datasets and the criticality of the domain, an explainable method is mandatory to facilitate decision-making interpretation. Thus, SaNDA prioritises explainability over efficiency by design. We evaluated our proposal against Random Forest as a baseline for explainable methods, and against gcForest as state-of-the-art for small datasets. We observed that our proposal outperforms Random Forest when there is more missing data and/or lower number of entries in the dataset, obtaining less favourable results over larger, well-curated datasets. It is also preferable than gcForest due to its explainability and privacy protection capabilities. Given the difficulties in obtaining complete, reliable data in the healthcare field, we consider that our proposal could be useful for practitioners.

NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory

The continued spread of COVID-19 seriously endangers the physical and mental health of people in all countries. It is an important method to establish inter agency COVID-19 detection and prevention system based on game theory through wireless communication and artificial intelligence. Federated learning (FL) as a privacy preserving machine learning framework has received extensive attention. From the perspective of game theory, FL can be regarded as a process in which multiple participants play games against each other to maximize their own interests. This requires that the user’s data is not leaked during the training process. However, existing studies have proved that the privacy protection capability of FL is insufficient. In addition, the existing way of realizing privacy protection through multiple rounds of communication between participants increases the burden of wireless communication. To this end, this paper considers the security model of FL based on game theory, and proposes our scheme, NVAS, a non-interactive verifiable privacy-preserving FL aggregation scheme in wireless communication environments. The NVAS can protect user privacy during FL training without unnecessary interaction between participants, which can better motivate more participants to join and provide high-quality training data. Furthermore, we designed a concise and efficient verification algorithm to ensure the correctness of model aggregation. Finally, the security and feasibility of the scheme are analyzed.

Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks.

Although evidence supporting the feasibility of large-scale mobile health (mHealth) systems continues to grow, privacy protection remains an important implementation challenge. The potential scale of publicly available mHealth applications and the sensitive nature of the data involved will inevitably attract unwanted attention from adversarial actors seeking to compromise user privacy. Although privacy-preserving technologies such as federated learning (FL) and differential privacy (DP) offer strong theoretical guarantees, it is not clear how such technologies actually perform under real-world conditions. Using data from the University of Michigan Intern Health Study (IHS), we assessed the privacy protection capabilities of FL and DP against the trade-offs in the associated model's accuracy and training time. Using a simulated external attack on a target mHealth system, we aimed to measure the effectiveness of such an attack under various levels of privacy protection on the target system and measure the costs to the target system's performance associated with the chosen levels of privacy protection. A neural network classifier that attempts to predict IHS participant daily mood ecological momentary assessment score from sensor data served as our target system. An external attacker attempted to identify participants whose average mood ecological momentary assessment score is lower than the global average. The attack followed techniques in the literature, given the relevant assumptions about the abilities of the attacker. For measuring attack effectiveness, we collected attack success metrics (area under the curve [AUC], positive predictive value, and sensitivity), and for measuring privacy costs, we calculated the target model training time and measured the model utility metrics. Both sets of metrics are reported under varying degrees of privacy protection on the target. We found that FL alone does not provide adequate protection against the privacy attack proposed above, where the attacker's AUC in determining which participants exhibit lower than average mood is over 0.90 in the worst-case scenario. However, under the highest level of DP tested in this study, the attacker's AUC fell to approximately 0.59 with only a 10% point decrease in the target's R2 and a 43% increase in model training time. Attack positive predictive value and sensitivity followed similar trends. Finally, we showed that participants in the IHS most likely to require strong privacy protection are also most at risk from this particular privacy attack and subsequently stand to benefit the most from these privacy-preserving technologies. Our results demonstrated both the necessity of proactive privacy protection research and the feasibility of the current FL and DP methods implemented in a real mHealth scenario. Our simulation methods characterized the privacy-utility trade-off in our mHealth setup using highly interpretable metrics, providing a framework for future research into privacy-preserving technologies in data-driven health and medical applications.

Real-Time Cost Optimization Approach Based on Deep Reinforcement Learning in Software-Defined Security Middle Platform

In today’s business environment, reducing costs is crucial due to the variety of Internet of Things (IoT) devices and security infrastructure. However, applying security measures to complex business scenarios can lead to performance degradation, making it a challenging task. To overcome this problem, we propose a novel algorithm based on deep reinforcement learning (DRL) for optimizing cost in multi-party computation software-defined security middle platforms (MPC-SDSmp) in real-time. To accomplish this, we first integrate fragmented security requirements and infrastructure into the MPC-SDSmp cloud model with privacy protection capabilities to reduce deployment costs. By leveraging the power of DRL and cloud computing technology, we enhance the real-time matching and dynamic adaptation capabilities of the security middle platform (Smp). This enables us to generate a real-time scheduling strategy for Smp resources that meet low-cost goals to reduce operating costs. Our experimental results demonstrate that the proposed method not only reduces the costs by 13.6% but also ensures load balancing, improves the quality-of-service (QoS) satisfaction by 18.7%, and reduces the average response time by 34.2%. Moreover, our solution is highly robust and better suited for real-time environments compared to the existing methods.