Improved Privacy Protection Research Articles

FHG-PR: a hybridized fuzzy-AHP and game theory model for assessing privacy risk on social media platforms

Social media platforms have become integral to our daily lives, enabling global connectivity and interaction. Current research provides general solutions for mitigating privacy risks, but it does not focus on particular platforms. However, the voluntary sharing of personal information on these platforms presents considerable privacy risks, highlighting the need for effective risk assessment mechanisms. This study presents a hybrid approach that uses the fuzzy Analytical Hierarchy Process (AHP) and game theory to estimate privacy risks on Meta and X. We used a fuzzy AHP to rank determinant variables based on surveys of social media users and professionals. These weighted criteria were then used in a Multi-Criteria Decision-Making (MCDM) framework using cooperative game theory to discover ways to lower privacy hazards. The model accurately analyzed the privacy threats on Meta and X, recommending alternate techniques for preventing breaches in data privacy. The cooperative game theory method allows stakeholders to collaborate on creating privacy-preserving measures. The findings emphasize the model’s ability to address platform-specific privacy threats, as well as its flexibility in other social media settings. The model’s potential for real-world application is demonstrated by its ability to provide practical risk reduction measures, which improve privacy protection for social media users.

A single parameter method for secure privacy preserving record linkage

ObjectivesData linkage is the process of matching records that refer to the same entities (often people) across databases. In applications such as health research or government services, the databases to be linked are often sensitive and cannot be shared between organisations. Privacy-preserving record linkage (PPRL) aims to overcome this challenge by facilitating the comparison of encoded or encrypted records without having to share sensitive data. Most existing PPRL techniques are based on heuristics and they have limitations in the privacy protection they offer, such as being vulnerable to certain cryptanalysis attacks. Furthermore, existing PPRL methods have multiple parameters, which, if not set properly by the user, can result in sub-optimal linkage quality and reduced privacy protection. ApproachWe present a novel PPRL method that uses random reference q-gram sets to generate bit-arrays that represent sensitive values. Our method has a single parameter to be set by the user that trades scalability with linkage quality and privacy protection. All other parameters are either data-driven or have strong bounds based on this user parameter. ResultsWe conceptually analyse our method and conduct experiments on multiple databases. The results demonstrate that our method provides high linkage quality and strong privacy protection while being scalable to link very large databases. ConclusionOur novel PPRL method provides high linkage quality, scalability, and improved privacy protection compared to existing PPRL methods such as Bloom filter encoding. A major advantage of our method is that it requires a single parameter to be set by the user.

Enhancing Privacy on Online Social Networs: A comparative study of encryption techniques

The ability to connect with people all over the world and share private information has made online social networks (OSNs) indispensable to contemporary communication. Because of the serious privacy and security concerns that come with their fast growth, protecting user data is of the utmost importance. In order to improve OSN privacy, this study examines several encryption techniques. It discusses symmetric encryption (ECC and RSA) and synthetic encryption (AES) as well as hybrid methods that combine the two types of encryption. Using examples from well-known sites such as LinkedIn, Facebook, and Twitter, the research assesses the efficacy and practicality of various strategies. Key management, performance implications, and user experience maintenance are some of the challenges that are addressed while adopting comprehensive encryption. To further improve privacy protection in OSNs, the paper delves into upcoming developments in encryption technology, such as decentralized encryption approaches and algorithms that are resistant to quantum computing. In order to better understand how to improve OSN security, this article will provide a thorough examination of encryption's function in protecting user privacy.

VLR-BPP: An intelligent virtual location replacement based bilateral privacy-preserving architecture for edge cloud systems

Mobile Crowdsourcing (MCS) has emerged as a significant edge-cloud computing paradigm in which workers perceive data at the network edge and report it to cloud-based computing services for processing, enabling the construction of various applications. Consequently, it is imperative to achieve Bilateral Location Privacy-Preserving (BLPP) to protect the privacy of both Data Requester (DR) and workers, as disclosing location information entails many sensitive details that can result in losses for DR and workers alike. The Local Differential Privacy (LDP) approach is widely employed in Privacy-Preserving (PP) techniques due to its inherent advantages, wherein owners release data with added noise, allowing for proactive customization of privacy strength without relying on any third party. However, the current state of LDP methods presents a dilemma: when privacy protection is strong, introducing excessive location noise can lead to a decrease in the accuracy of task-worker matching, while a high rate of task-worker matching necessitates the compromise of privacy strength. In this paper, an intelligent Virtual Location Replacement based enhanced Bilateral Privacy-Preserving (VLR-BPP) architecture is proposed to improve privacy protection strength and matching accuracy in MCS simultaneously. Within the VLR-BPP architecture, a Bipartite-Graph-based Matrix Completion (BGMC) model is employed to establish the spatiotemporal correlations among data. Then, a Virtual Location Replacement (VLR) strategy is proposed to obfuscate the locations of tasks or workers to their highly correlated virtual location before publishing. Based on VLR, three preemptive location virtualization approaches are introduced: Only Task Location Virtual (OTLV), Only Workers Location Virtual (OWLV), and Both Task and Workers Location Virtual (BTWLV). For workers and DR, Randomized Response (RR) techniques and Random Matrix Multiplication Mechanism (RMM) are used to implement LDP independently. A greedy algorithm is adopted to recruit workers for tasks. In response to the data submitted by workers, BGMC imputation mechanism is utilized to enhance data quality. Finally, simulations based on real-world datasets demonstrate that the performance of our architecture surpasses existing state-of-the-art methods in privacy protection and data collection quality by 18.92∼38.17% and 15.49∼50.77%, respectively.

Channel Adaptive and Sparsity Personalized Federated Learning for Privacy Protection in Smart Healthcare Systems.

With the booming development of Smart Healthcare Systems (SHSs), employing federated learning (FL) in SHS devices has become a research hotspot. FL, as a distributed learning framework, can train models without sharing the original data among users, and then protect the user privacy. Existing research has proposed many methods to improve the security and efficiency of FL, which may not fully consider the characteristics of SHSs. Specifically, the requirements of privacy protection and efficiency pose significant challenges to FL. Current studies have struggled to balance privacy security and efficiency, and the degradation of model training efficiency in SHSs can be critical to patient health. Therefore, to improve the privacy protection of healthcare data and ensure communication efficiency, this work proposes a novel personalized FL framework based on Communication quality and Adaptive Sparsification (pFedCAS). In order to achieve privacy protection, a control unit is proposed and introduced to adjust the sparsity of the local model adaptively. To further improve the training efficiency, a selection unit is added during global model aggregation to select suitable clients for parameter updates. Finally, we validate the proposed method operated on the HAM10000 dataset. Simulation results validate that pFedCAS can not only improve privacy protection, but also gain an improvement of 15% in training accuracy and a reduction of 30% in training costs based on communication quality. The simulation results also validate the excellent robustness of pFedCAS to non-iid data.

A Realizable Data Encryption Strategy

As technology continues to advance, data has become an increasingly important element in the sphere of Information Technology. However, enormous data generated by devices presents a major challenge in handling it in real time. Data encryption is a crucial component in ensuring data security and privacy during its transmission in network. Unfortunately, many applications disregard data encryption in order to achieve higher performance. The work proposes a solution to this problem by introducing a data encryption process that is, the Realizable Data Encryption Strategy (RDES) and Deoxyribonucleic Acid (DNA) computing, a revolutionary cryptographic method that improves information security by preventing authorized access to sensitive data, being used. Information security is improved by DNA symmetric cryptography being suggested. The outcomes show that plain-text encryption is a very secure procedure. The RDES approach is designed to improve privacy protection within the constraints of real-time processing. By implementing the RDES approach, data privacy and security can be significantly enhanced without compromising performance.

Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics

The development of artificial intelligence (AI)-based melanoma classifiers typically calls for large, centralized datasets, requiring hospitals to give away their patient data, which raises serious privacy concerns. To address this concern, decentralized federated learning has been proposed, where classifier development is distributed across hospitals. To investigate whether a more privacy-preserving federated learning approach can achieve comparable diagnostic performance to a classical centralized (ie, single-model) and ensemble learning approach for AI-based melanoma diagnostics. This multicentric, single-arm diagnostic study developed a federated model for melanoma-nevus classification using histopathological whole-slide images prospectively acquired at 6 German university hospitals between April 2021 and February 2023 and benchmarked it using both a holdout and an external test dataset. Data analysis was performed from February to April 2023. All whole-slide images were retrospectively analyzed by an AI-based classifier without influencing routine clinical care. The area under the receiver operating characteristic curve (AUROC) served as the primary end point for evaluating the diagnostic performance. Secondary end points included balanced accuracy, sensitivity, and specificity. The study included 1025 whole-slide images of clinically melanoma-suspicious skin lesions from 923 patients, consisting of 388 histopathologically confirmed invasive melanomas and 637 nevi. The median (range) age at diagnosis was 58 (18-95) years for the training set, 57 (18-93) years for the holdout test dataset, and 61 (18-95) years for the external test dataset; the median (range) Breslow thickness was 0.70 (0.10-34.00) mm, 0.70 (0.20-14.40) mm, and 0.80 (0.30-20.00) mm, respectively. The federated approach (0.8579; 95% CI, 0.7693-0.9299) performed significantly worse than the classical centralized approach (0.9024; 95% CI, 0.8379-0.9565) in terms of AUROC on a holdout test dataset (pairwise Wilcoxon signed-rank, P < .001) but performed significantly better (0.9126; 95% CI, 0.8810-0.9412) than the classical centralized approach (0.9045; 95% CI, 0.8701-0.9331) on an external test dataset (pairwise Wilcoxon signed-rank, P < .001). Notably, the federated approach performed significantly worse than the ensemble approach on both the holdout (0.8867; 95% CI, 0.8103-0.9481) and external test dataset (0.9227; 95% CI, 0.8941-0.9479). The findings of this diagnostic study suggest that federated learning is a viable approach for the binary classification of invasive melanomas and nevi on a clinically representative distributed dataset. Federated learning can improve privacy protection in AI-based melanoma diagnostics while simultaneously promoting collaboration across institutions and countries. Moreover, it may have the potential to be extended to other image classification tasks in digital cancer histopathology and beyond.

Evaluation of Chinese HIV Mobile Apps by Researchers and Patients With HIV: Quality Evaluation Study.

Against the backdrop of globalization, China remains one of the most heavily burdened countries in Asia with regard to AIDS. However, many high-risk groups and patients affected by AIDS may be less likely to actively seek care from medical institutions because of fear of experiencing shame or discrimination. Mobile apps provide a promising avenue for supporting the prevention, diagnosis, and treatment of AIDS. However, a comprehensive systematic evaluation of these mobile apps' functionality and quality has not been conducted yet. This study aims to identify the available mobile apps for AIDS in China, assess and discuss the functional features and quality of these Chinese AIDS mobile apps, and offer decision support for patients and clinical practitioners in accessing high-quality AIDS mobile apps. Furthermore, based on the evaluation results, recommendations for improvement will be provided. A systematic search was conducted on the Qimai app data platform, the Aladdin WeChat applet data platform, and WeChat to identify mobile apps related to AIDS. A snowball sampling method was used to supplement the potentially overlooked apps. The selected mobile apps underwent a rigorous screening process based on unified criteria. Subsequently, assessments were independently undertaken by 3 separate researchers and 2 patients with HIV, using both the Mobile App Rating Scale (MARS) and the User Mobile App Rating Scale (uMARS). Quantitative interpretations of the data were facilitated by the MedCalc statistical software (version 20.217, MedCalc Software). A total of 2901 AIDS mobile apps were included in the study, with 2897 identified through information retrieval and an additional 4 added via snowball sampling. After a rigorous selection process, 21 apps were determined to be usable. Among them, the Hong Feng Wan app achieved the highest combined average score, calculated based on the MARS (3.96, SD 0.33) and uMARS (4.47, SD 0.26). Overall, there was no significant correlation between MARS and uMARS (rapp quality total score=0.41; P=.07; rsubjective quality=0.39; P=.08). A notable issue was the widespread lack of user privacy protection, with only 24% (5/21) of the apps offering this feature. The number of available Chinese AIDS mobile apps is limited, with WeChat applets dominating the market. Nonetheless, the performance of WeChat mini-apps is generally inferior to that of independent apps, and there may be significant discrepancies between assessments conducted by researchers and those provided by genuine end users, emphasizing the necessity of involving real users in the development and evaluation of HIV mobile apps. In addition, developers of these Chinese HIV mobile apps need to devote attention to improving privacy protection mechanisms, in addition to considering the evaluations of researchers and real users. This will help attract more users and increase user loyalty.

Blockchain based solutions for privacy-preserving authentication and authorization in networks

Securing sensitive user information and maintaining privacy during authentication and authorization processes is crucial in today’s interconnected digital world. This study investigates new approaches to tackle these obstacles by combining blockchain technology with Trusted Execution Environment (TEE). The proposed system introduces a new method that combines the advantages of both technologies to create a strong and privacy-focused framework for network security. The study assesses the effectiveness of the suggested system based on key parameters, focusing specifically on Transaction Throughput (TPS) and Latency. The combination of blockchain and TEE aims to improve privacy protection and security protocols, providing a dependable solution for privacy-focused authentication and authorization in network settings. The experiments confirmed the efficacy of the proposed model. The Transaction Throughput (TPS) reached 296 transactions per second, demonstrating the system’s scalability and capacity to manage a large number of transactions. The Latency was measured at 54 milliseconds, indicating a fast and responsive authentication and authorization process. The research enhances the development of privacy-preserving solutions by emphasizing the importance of the blockchain and TEE collaboration. The proposed model surpasses performance expectations set by traditional methods, positioning itself as a significant advancement in network security. The results highlight the system’s ability to significantly improve privacy protection in digital transactions and set the stage for advancements in secure, efficient, and privacy-focused authentication and authorization systems. between the quantity demand and the price.

Improved privacy protection technique for enhancing security of real-time video surveillance

Improved privacy protection technique for enhancing security of real-time video surveillance

A study of innovations in legal governance with respect to the safety of artificial intelligence

Abstract This paper aims to promote the safe development of artificial intelligence and improve legal policies. Combined with the cluster analysis algorithm, it analyzes the safety risks as well as legal defects of artificial intelligence. The Laplace matrix is derived using the similarity matrix, and the feature vector space is constructed by analyzing the associated features of artificial intelligence safety. Combining the spectral clustering algorithm, legal assessment indexes for artificial intelligence safety were constructed. The modular metric value method is utilized to assess the clustering effect of laws on the safety of artificial intelligence. Analyzing the security risks of artificial intelligence, improved legal policies are proposed from the perspective of technology and privacy. The results show that the effect of improving privacy protection policy on privacy protection is 0.85, and the effect of clarifying subject rights is 0.9. The introduction of laws should consider social ethics, and the effect degree of ethical principles is 0.75. Clarifying subject rights can help avoid technological risks to a certain extent, and improving privacy protection policies can help protect users’ privacy.

IPMRSS: An Improved privacy-preserving medical record searching scheme for intelligent diagnosis in IoMT

With the rapid development of artificial intelligence, the Internet of Things and next generation mobile communication, the Internet of Medical Things (IoMT) as a typical application has attracted increasing attention as a typical application thereof owing to its convenience and practicality. To address certain privacy preservation issues concerning accessing patients records from past case databases as well as diagnosed patients’ sensitive information on the Internet of Medical Things, an IoMT-secure privacy-preserving medical record search scheme (PMRSS) based on ELGamal blind signatures is proposed. In this paper, we first show that certain security flaws exists in the PMRSS. To address these flaws, we propose an improved privacy protection medical record searching scheme (iPMRSS) in IoMT for intelligent diagnoses based on the elliptical curve discrete logarithm problem (ECDLP). Security analysis demonstrates that our iPMRSS provides correctness, identity privacy is ensured based on the hard problem assumption of Elliptical curve discrete logarithm problem. Data security is satisfied based on the security of the Blind ECDSA Signature. Performance evaluations demonstrate that our theoretical results are consistent with simulations, thereby a comparative analysis with related state of art techniques show that our proposed iPMRSS scheme in about 1.5 ms has shorter time cost under some security level.

Federated malware detection based on many‐objective optimization in cross‐architectural IoT

SummaryWith the rising adoption of the Internet of Things (IoT) across a variety of industries, malware is increasingly targeting the large number of IoT devices that lack adequate protection. Malware hunting is challenging in the IoT due to the variety of instruction set architectures of devices, as shown by the differences in the relevant characteristics of malware on different platforms. There are also serious concerns about resource utilization and privacy leaks in the development of conventional detection models. This study suggests a novel federated malware detection framework based on many‐objective optimization (FMDMO) for the IoT to overcome the problems. First, the framework provides a cross‐platform compatible basis with the federated mechanism as the backbone, while avoiding raw data sharing to improve privacy protection. Second, an intelligent optimization‐based client selection method is designed for four objectives: learning performance, architectural selection deviation, time consumption, and training stability, which leads malware detection to retain a high degree of cross‐architectural generalization while enhancing training efficiency. Based on a large IoT malware dataset we constructed, containing 62,515 malware samples across seven typical architectures, the FMDMO is evaluated comprehensively in three scenarios. The experimental results demonstrate the FMDMO substantially enhances the model's cross‐platform detection performance while preserving effective training and flexibility.

Research on the Impact of Privacy Information Sensitivity on Consumers' Purchase Intention in E-commerce Livestreaming

Against the backdrop of rapid development in digital and network technologies, privacy issues in e-commerce live streaming have gradually drawn consumers' attention. How to effectively use consumers' personal information to promote the high-quality development of e-commerce live streaming is a key issue of concern in academia. Based on sample data, this study summarizes the current status of consumers' privacy sensitivity when watching e-commerce live streaming, explores the impact of privacy sensitivity on consumers' purchase intention, and finds that privacy concerns negatively affect consumers' purchase intention, while privacy protection and privacy fatigue positively affect consumers' purchase intention. Perceived trust and perceived risk play a mediating role in the relationship between consumers' privacy sensitivity and purchase intention. Based on these findings, it is recommended that e-commerce live streaming platforms actively formulate and improve privacy protection policies to further protect consumers' privacy information, while establishing joint credit evaluation standards to enhance consumer trust. Merchants should comply with privacy policies and strengthen brand building. Government departments should enhance the diversity and effectiveness of privacy protection education and use innovative technologies to create a good social environment for consumers' purchasing behavior on e-commerce live streaming platforms.

Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry

Since its introduction in 2006, differential privacy has emerged as a predominant statistical tool for quantifying data privacy in academic works. Yet despite the plethora of research and open-source utilities that have accompanied its rise, with limited exceptions, differential privacy has failed to achieve widespread adoption in the enterprise domain. Our study aims to shed light on the fundamental causes underlying this academic-industrial utilization gap through detailed interviews of 24 privacy practitioners across 9 major companies. We analyze the results of our survey to provide key findings and suggestions for companies striving to improve privacy protection in their data workflows and highlight the necessary and missing requirements of existing differential privacy tools, with the goal of guiding researchers working towards the broader adoption of differential privacy. Our findings indicate that analysts suffer from lengthy bureaucratic processes for requesting access to sensitive data, yet once granted, only scarcely-enforced privacy policies stand between rogue practitioners and misuse of private information. We thus argue that differential privacy can significantly improve the processes of requesting and conducting data exploration across silos, and conclude that with a few of the improvements suggested herein, the practical use of differential privacy across the enterprise is within striking distance.

Research on the influencing factors of users' information processing in online health communities based on heuristic-systematic model.

With the rapid development of the Internet and the normalization of COVID-19 epidemic prevention and control, Online health communities (OHCs) have gradually become one of the important ways for people to obtain health information, and users have to go through a series of information processing when facing the massive amount of data. Understanding the factors influencing user information processing is necessary to promote users' health literacy, health knowledge popularization and health behavior shaping. Based on the Heuristic-Systematic Model (HSM), Information Ecology Theory, Privacy Trade-Off and Self-Efficacy Theory, we constructed a model of factors influencing user information processing in online health communities. We found that information quality and emotional support had indirect effects on heuristic and systematic information processing, and these effects were mediated by privacy concerns and self-efficacy. In our research model, systematic information processing was most positively influenced directly by self-efficacy. Privacy concerns had a direct negative correlation with both dual information processing pathways. Therefore, OHCs managers should develop relevant regulations to ensure the information quality in OHCs and improve privacy protection services to promote user information processing by improving users' self-efficacy and reducing their privacy concerns. Providing a user-friendly and interactive environment for users is also recommended to create more emotional support, thus facilitating more systematic information processing.

An Improved Privacy Protection Algorithm for Multimodal Data Fusion

With the rapid development of Internet technology, the use and sharing of data have brought great opportunities and challenges to mankind. On the one hand, the development of data sharing and analysis technology has promoted the improvement of economic and social benefits. On the other hand, protecting private information has become an urgent issue in the Internet era. In addition, the amount and type of information data are also increasing. At present, most algorithms can only encrypt a single type of small-scale data, which cannot meet the current data environment. Therefore, it is very necessary to study the privacy protection algorithm of multimodal data fusion. To improve the security of privacy protection algorithm, combined with the idea of multimode, this paper combines the improved traditional spatial steganography algorithm LSB matching method and the improved traditional transform domain steganography algorithm DCT with AES encryption algorithm after modifying the S-box and then combines it with image stitching technology, so as to realize a safe and reliable privacy protection algorithm of multimode information fusion. The algorithm completes the hidden communication of private information, which not only ensures that the receiver can accurately recover private information in the process of information transmission but also greatly improves the security of private information transmission.

Data privacy protection in scientific publications: process implementation at a pharmaceutical company

BackgroundSharing anonymized/de-identified clinical trial data and publishing research outcomes in scientific journals, or presenting them at conferences, is key to data-driven scientific exchange. However, when data from scientific publications are linked to other publicly available personal information, the risk of reidentification of trial participants increases, raising privacy concerns. Therefore, we defined a set of criteria allowing us to determine and minimize the risk of data reidentification. We also implemented a review process at Takeda for clinical publications prior to submission for publication in journals or presentation at medical conferences.Methodss, manuscripts, posters, and oral presentations containing study participant information were reviewed and the potential impact on study participant privacy was assessed. Our focus was on direct (participant ID, initials) and indirect identifiers, such as sex, age or geographical indicators in rare disease studies or studies with small sample size treatment groups. Risk minimization was sought using a generalized presentation of identifier-relevant information and decision-making on data sharing for further research. Additional risk identification was performed based on study participant/personnel parameters present in materials destined for the public domain. The potential for participant/personnel identification was then calculated to facilitate presentation of meaningful but de-identified information.ResultsThe potential for reidentification was calculated using a risk ratio of the exposed versus available individuals, with a value above the threshold of 0.09 deemed an unacceptable level of reidentification risk. We found that in 13% of Takeda clinical trial publications reviewed, either individuals could potentially be reidentified (despite the use of anonymized data sets) or inappropriate data sharing plans could pose a data privacy risk to study participants. In 1/110 abstracts, 58/275 manuscripts, 5/87 posters and 3/58 presentations, changes were necessary due to data privacy concerns/rules. Despite the implementation of risk-minimization measures prior to release, direct and indirect identifiers were found in 11% and 34% of the analysed documents, respectively.ConclusionsRisk minimization using de-identification of clinical trial data presented in scientific publications and controlled data sharing conditions improved privacy protection for study participants. Our results also suggest that additional safeguards should be implemented to ensure that higher data privacy standards are met.

Taxonomy-based data representation for data mining: an example of the magnitude of risk associated with H. pylori infection

BackgroundThe amount of available and potentially significant data describing study subjects is ever growing with the introduction and integration of different registries and data banks. The single specific attribute of these data are not always necessary; more often, membership to a specific group (e.g. diet, social ‘bubble’, living area) is enough to build a successful machine learning or data mining model without overfitting it. Therefore, in this article we propose an approach to building taxonomies using clustering to replace detailed data from large heterogenous data sets from different sources, while improving interpretability. We used the GISTAR study data base that holds exhaustive self-assessment questionnaire data to demonstrate this approach in the task of differentiating between H. pylori positive and negative study participants, and assessing their potential risk factors. We have compared the results of taxonomy-based classification to the results of classification using raw data.ResultsEvaluation of our approach was carried out using 6 classification algorithms that induce rule-based or tree-based classifiers. The taxonomy-based classification results show no significant loss in information, with similar and up to 2.5% better classification accuracy. Information held by 10 and more attributes can be replaced by one attribute demonstrating membership to a cluster in a hierarchy at a specific cut. The clusters created this way can be easily interpreted by researchers (doctors, epidemiologists) and describe the co-occurring features in the group, which is significant for the specific task.ConclusionsWhile there are always features and measurements that must be used in data analysis as they are, the use of taxonomies for the description of study subjects in parallel allows using membership to specific naturally occurring groups and their impact on an outcome. This can decrease the risk of overfitting (picking attributes and values specific to the training set without explaining the underlying conditions), improve the accuracy of the models, and improve privacy protection of study participants by decreasing the amount of specific information used to identify the individual.

Research on Blockchain-Based E-Bidding System

In the field of modern bidding, electronic bidding leads a new trend of development, convenience and efficiency and other significant advantages effectively promote the reform and innovation of China’s bidding field. Nowadays, most systems require a strong and trusted third party to guarantee the integrity and security of the system. However, with the development of blockchain technology and the rise of privacy protection, researchers has begun to emphasize the core concept of decentralization. This paper introduces a decentralized electronic bidding system based on blockchain and smart contract. The system uses blockchain to replace the traditional database and uses chaincode to process business logic. In data interaction, encryption techniques such as zero-knowledge proof based on graph isomorphism are used to improve privacy protection, which improves the anonymity of participants, the privacy of data transmission, and the traceability and verifiable of data. Compared with other electronic bidding systems, this system is more secure and efficient, and has the nature of anonymous operation, which fully protects the privacy information in the bidding process.