Sensitive Data Leakage Research Articles

Telemedicine data secure sharing scheme based on heterogeneous federated learning

The forward triage characteristic of telemedicine highlights its importance again in the COVID-19 pandemic. Telemedicine can provide timely emergency response in the case of environmental or biological hazards, and the patient’s medical privacy data generated in this process can also accelerate the establishment of models for preventing and treating infectious diseases. However, the reuse process of telemedicine user privacy data based on federated learning also faces significant challenges. Differences in regions, economic levels, and grades lead to heterogeneous data and resource-constrained environments, seriously damaging the federated learning process. Besides, the weak password authentication of medical terminals and eavesdropping attacks on transmission channels may cause illegal access to terminals and platforms and leakage of sensitive data. This paper proposed a telemedicine data secure-sharing scheme based on heterogeneous federated learning. Specifically, we proposed a heterogeneous federated learning scheme with model alignment to guide telemedicine practice through the reuse of telemedicine data; in addition, we designed an SM9 threshold identity authentication scheme to guarantee that the patient’s medical privacy data is protected from leakage during the federated learning process. We evaluated our scheme using two third-party medical datasets. The evaluation results indicate that this scheme can still assist the federated learning process in resisting data heterogeneity and resource constraints with almost no performance cost.

The Medium is the Message: How Secure Messaging Apps Leak Sensitive Data to Push Notification Services

Like most modern software, secure messaging apps rely on thirdparty components to implement important app functionality. Although this practice reduces engineering costs, it also introduces the risk of inadvertent privacy breaches due to misconfiguration errors or incomplete documentation. Our research investigated secure messaging apps' usage of Google's Firebase Cloud Messaging (FCM) service to send push notifications to Android devices. We analyzed 21 popular secure messaging apps from the Google Play Store to determine what personal information these apps leak in the payload of push notifications sent via FCM. Of these apps, 11 leaked metadata, including user identifiers (10 apps), sender or recipient names (7 apps), and phone numbers (2 apps), while 4 apps leaked the actual message content. Furthermore, none of the data we observed being leaked to FCM was specifically disclosed in those apps' privacy disclosures. We also found several apps employing strategies to mitigate this privacy leakage to FCM, with varying levels of success. Of the strategies we identified, none appeared to be common, shared, or well-supported. We argue that this is fundamentally an economics problem: incentives need to be correctly aligned to motivate platforms and SDK providers to make their systems secure and private by default.

A Blockchain-Based Authentication Mechanism for Enhanced Security.

Passwords are the first line of defence against preventing unauthorised access to systems and potential leakage of sensitive data. However, the traditional reliance on username and password combinations is not enough protection and has prompted the implementation of technologies such as two-factor authentication (2FA). While 2FA enhances security by adding a layer of verification, these techniques are not impervious to threats. Even with the implementation of 2FA, the relentless efforts of cybercriminals present formidable obstacles in securing digital spaces. The objective of this work is to implement blockchain technology as a form of 2FA. The findings of this work suggest that blockchain-based 2FA methods could strengthen digital security compared to conventional 2FA methods.

Physical layer encryption-based secure slicing in 5G RAN with hybrid-trusted links

In a 5G radio access network (RAN), network slicing enables dividing a single RAN infrastructure into multiple logical networks, efficiently accommodating services with diverse requirements. Although RAN slicing can help improve resource efficiency and reduce network costs, it is accompanied by various security risks. One of the security threats in RAN slicing is potential eavesdropping, resulting in the leakage of sensitive data within slices. Encryption technologies have been developed to address the eavesdropping problem at different layers in optical networks. We focus on physical layer encryption since it has been demonstrated beneficial in line-speed processing, low latency, and small encryption overhead. The problem of utilizing physical layer encryption technologies to achieve secure RAN slices remains unexplored since physical layer encryption introduces additional hardware costs. In this paper, we study how to realize secure RAN slicing based on physical layer encryption in a metro aggregation network that consists of hybrid-trusted links (i.e., links with different risks for eavesdropping). We propose an integer linear programming (ILP) model and an auxiliary graph-based heuristic for small-scale and large-scale networks, respectively. The objective is to maximize the number of deployed slices and minimize the total cost of secure slice deployment, which includes the costs of servers, line cards (LCs), encryption cards (ECs), and bandwidth resources. To evaluate the benefit of encryption, we compare it with a detour solution, which protects slices by routing through trusted links (i.e., where no additional hardware for encryption is deployed). Simulation results show that the encryption-based solution exhibits a lower cost than the benchmark when the same number of slices are deployed, and it can reduce the blocking ratio by up to 8.5% as slice requests increase. In addition, the average latency of slices is also reduced by up to 14.6%.

An Identity Authentication Method Of Configuration Terminal Based On Audio Feature Recognition In Smart Distribution Network

Abstract A smart distribution network contains a large number of data acquisition terminals used to monitor the operation status of the distribution network and control the operation parameters, some of which are located in outdoor and other unsafe environments. If the attacker operates and configures the distribution network terminal by pretending to be a legitimate configuration device, it will become a springboard to infiltrate the main station of the internal network or misreport fault information, which may result in the leakage of sensitive data and even destroy the running state of the equipment. Identity authentication is the first line of defense to ensure the safe and stable operation of equipment. At present, the commonly used identity authentication technologies such as certificate authentication and key authentication have certain security vulnerabilities at different levels, which make it increasingly difficult to prevent the ever-changing network attack technology. According to the characteristics of physical unclonable facility (PUF), which is used to describe the hidden characteristics of the device and is directly related to the hardware, this paper proposes a method to authenticate the validity of the power distribution network configuration terminal based on the audio feature, which collects the frequency response characteristics of the hardware from the audio sensor of the configuration device as the identification information to identify the validity of the configuration device.

Enhancing Monitoring Performance: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models.

In today's digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices.

Detecting vulnerabilities in website using multiscale approaches: based on case study

In the realm of modern web applications, security stands as an utmost priority. To address this critical concern, we've developed a versatile Python script with the primary goal of proactively identifying vulnerabilities and thwarting transient attacks. Leveraging various libraries, this tool comprehensively covers a broad spectrum of threats, including SQL injection (SQLi), cross-site scripting (XSS), cross-site request forgery (CSRF), sensitive data leakage, security misconfiguration, distributed denial-of-service (DDoS) vulnerabilities, and secure socket layer (SSL) or transport layer security (TLS). This Python-based solution prioritizes adaptability, ensuring seamless integration of future updates to effectively combat evolving threats. Utilizing innovative methods such as SQLi and XSS payload injection, the script assesses the susceptibility of input fields. And addressing CSRF vulnerabilities, the script generates and validates tokens, fortifying defenses against unauthorized actions. Employing pattern analysis, it combats sensitive data exposure and security misconfigurations, adeptly identifying elements like credit card numbers, passwords, and headers. Furthermore, the script enhances overall security by scrutinizing SSL/TLS protocols and monitoring port accessibility. It reinforces DDoS detection by actively monitoring traffic patterns, identifying anomalies, and proactively averting disruptions.

Utilizing Watermarking Technique for Detecting Data Leakage in Cloud Environment

Ensuring safety in dossier management is paramount due to the immense value of stored information. While hackers are often attributed to security breaches, the reality is that a significant portion of data loss stems from insiders. In traditional setups, the transfer of critical data from suppliers to trusted entities is a frequent occurrence. Preserving the security and integrity of these transactions is crucial to meeting the increasing demands of consumers. Any leakage of sensitive data exposes customers to potential risks from the outset. Therefore, establishing secure channels for data transfer between suppliers and recipients is imperative. This project proposes a solution for detecting data leaks using watermarking technology, which detects tampering attempts and identifies the source of leaked information. The system operates within a cloud environment, ensuring accessibility and scalability.

Federated learning with blockchain-based model aggregation and incentives

Federated learning is a privacy-preserving machine learning technique that allows mutually distrusting parties to collaboratively train a model without sharing their data. Most federated learning techniques require a centralized aggregator that stores and aggregates models received from multiple parties. However, having a centralized entity may lead to a single point of failure problem. Another problem of federated learning is the leakage of sensitive data through model updates. To address these issues, we propose a Blockchain-based protocol for federated learning. Our protocol uses Blockchain as a model aggregator solving single-point-of-failure problems. Also, we use a Blockchain-based privacy-preserving technique to avoid data leakage problems. We also incorporate a Blockchain-based incentive distribution module to distribute incentives to model contributors. We perform experiments with well-known datasets and show that the proposed model's accuracy is close to that of a centralized aggregator. We also show the overhead of Blockchain by implementing the protocol and running it on Ethereum Blockchain.

UP-SDCG: A Method of Sensitive Data Classification for Collaborative Edge Computing in Financial Cloud Environment

The digital transformation of banks has led to a paradigm shift, promoting the open sharing of data and services with third-party providers through APIs, SDKs, and other technological means. While data sharing brings personalized, convenient, and enriched services to users, it also introduces security risks, including sensitive data leakage and misuse, highlighting the importance of data classification and grading as the foundational pillar of security. This paper presents a cloud-edge collaborative banking data open application scenario, focusing on the critical need for an accurate and automated sensitive data classification and categorization method. The regulatory outpost module addresses this requirement, aiming to enhance the precision and efficiency of data classification. Firstly, regulatory policies impose strict requirements concerning data protection. Secondly, the sheer volume of business and the complexity of the work situation make it impractical to rely on manual experts, as they incur high labor costs and are unable to guarantee significant accuracy. Therefore, we propose a scheme UP-SDCG for automatically classifying and grading financially sensitive structured data. We developed a financial data hierarchical classification library. Additionally, we employed library augmentation technology and implemented a synonym discrimination model. We conducted an experimental analysis using simulation datasets, where UP-SDCG achieved precision surpassing 95%, outperforming the other three comparison models. Moreover, we performed real-world testing in financial institutions, achieving good detection results in customer data, supervision, and additional in personally sensitive information, aligning with application goals. Our ongoing work will extend the model’s capabilities to encompass unstructured data classification and grading, broadening the scope of application.

A verifiable EVM-based cross-language smart contract implementation scheme for matrix calculation

The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses, which involve the calculation of complex functions, such as matrix operations. However, complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine (EVM)-based smart contract platforms due to their distributed security environment limitations. Existing off-chain methods often result in a significant reduction in contract execution efficiency, thus a platform software development kit interface implementation method has become a feasible way to reduce overheads, but this method cannot verify operation correctness and may leak sensitive user data. To solve the above problems, we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations, especially matrix operations, which can guarantee operation correctness and user privacy while ensuring computational efficiency. In this scheme, a verifiable interaction process is designed to verify the computation process and results, and a matrix blinding technology is introduced to protect sensitive user data in the calculation process. The security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.

Blockchain-Based System and Methods for Sensitive Data Transactions

A blockchain-based processing framework for sensitive data is proposed. The smart network comprises a blockchain module, an electronic contract (E-contract) layer node, and a Software-as-a-Service (SaaS) layer module. The underlying blockchain module provides technical support, such as virtual machines, consensus algorithms, transaction verification mechanisms, and accounting mechanisms. The E-contract layer module provides a distributed application service and uses the blockchain technology (BT) to support it. In addition, it runs the code of the sensitive data transaction system and the code created by the algorithm for encoding generation at the E-contract layer. The SaaS layer module offers a cloud-platform service that allows each party to easily engage in business collaboration via web portals or clients. The proposed smart system is used by each party get involved in the production of sensitive data. The final sensitive data are produced by the final data generator, and other modules involved in the process of data production are unaware of the final data. This approach prevents the leakage of sensitive data into the circulation.

TROP: TRust-aware OPportunistic Routing in NoC with Hardware Trojans

Multiple software and hardware intellectual property (IP) components are combined on a single chip to form Multi-Processor Systems-on-Chips (MPSoCs). Due to the rigid time-to-market constraints, some of the IPs are from outsourced third parties. Due to the supply-chain management of IP blocks being handled by unreliable third-party vendors, security has grown as a crucial design concern in the MPSoC. These IPs may get exposed to certain unwanted practises like the insertion of malicious circuits called Hardware Trojan (HT) leading to security threats and attacks, including sensitive data leakage or integrity violations. A Network-on-Chip (NoC) connects various units of an MPSoC. Since it serves as the interface between various units in an MPSoC, it has complete access to all the data flowing through the system. This makes NoC security a paramount design issue. Our research focuses on a threat model where the NoC is infiltrated by multiple HTs that can corrupt packets. Data integrity verified at the destination’s network interface (NI) triggers re-transmissions of packets if the verification results in an error. In this article, we propose an opportunistic trust-aware routing strategy that efficiently avoids HT while ensuring that the packets arrive at their destination unaltered. Experimental results demonstrate the successful movement of packets through opportunistically selected neighbours along a trust-aware path free from the HT effect. We also observe a significant reduction in the rate of packet re-transmissions and latency at the expense of incurring minimum area and power overhead.

Wasserstein Generative Adversarial Networks Based Differential Privacy Metaverse Data Sharing.

Although differential privacy metaverse data sharing can avoid privacy leakage of sensitive data, randomly perturbing local metaverse data will lead to an imbalance between utility and privacy. Therefore, this work proposed models and algorithms of differential privacy metaverse data sharing using Wasserstein generative adversarial networks (WGAN). Firstly, this study constructed the mathematical model of differential privacy metaverse data sharing by introducing appropriate regularization term related to generated data's discriminant probability into WGAN. Secondly, we established basic model and algorithm for differential privacy metaverse data sharing using WGAN based on the constructed mathematical model, and theoretically analyzed basic algorithm. Thirdly, we established federated model and algorithm for differential privacy metaverse data sharing using WGAN by serialized training based on basic model, and theoretically analyzed federated algorithm. Finally, based on utility and privacy metrics, we conducted a comparative analysis for the basic algorithm of differential privacy metaverse data sharing using WGAN, and experimental results validate theoretical results, which show that algorithms of differential privacy metaverse data sharing using WGAN maintaining equilibrium between privacy and utility.

A fully secure lattice-based signcryption with designated equality test in standard model

In hospital information systems, large volumes of electronic diagnostic records (EDRs) take up most of the storage space. While cloud server providers can reduce the local storage burden on hospitals and provide data-sharing services, the potential threat of sensitive data leakage and non-traceability of diagnoses prevents hospitals from uploading patients' diagnostic records to remote cloud servers directly. Therefore, to address security and traceability issues, we propose a new construction of post-quantum secure signcryption scheme with a designated equality test based on lattices (LB-SCDET) in this paper. Our LB-SCDET scheme allows a designated tester to perform equality tests on signcrypt-ciphertexts of EDRs without leaking what the EDRs actually are. Compared to the recent LB-SCET scheme proposed by Le et al., our LB-SCDET scheme implements a designated mechanism and is secure against offline message recovery attacks (OMRA). The comparison shows that our scheme enjoys a higher level of security, albeit the ciphertext size is slightly larger. Finally, we prove the scheme to be secure under the hardness of the Learning-with-Errors problem and unidirectionality of the Short-Integer-Solution problem. To be of independent interest, we show that the LB-SCET scheme fails to achieve the claimed IND-CCA security.

PLA—A Privacy-Embedded Lightweight and Efficient Automated Breast Cancer Accurate Diagnosis Framework for the Internet of Medical Things

The Internet of Medical Things (IoMT) can automate breast tumor detection and classification with the potential of artificial intelligence. However, the leakage of sensitive data can cause harm to patients. To address this issue, this study proposed an intrauterine breast cancer diagnosis method, namely “Privacy-Embedded Lightweight and Efficient Automated (PLA)”, for IoMT, which represents an approach that combines privacy-preserving techniques, efficiency, and automation to achieve our goals. Firstly, our model is designed to achieve lightweight classification prediction and global information processing of breast cancer by utilizing an advanced IoMT-friendly ViT backbone. Secondly, PLA protects patients’ privacy by federated learning, taking the classification task of breast cancer as the main task and introducing the texture analysis task of breast cancer images as the auxiliary task to train the model. For our PLA framework, the classification accuracy is 0.953, the recall rate is 0.998 for the best, the F1 value is 0.969, the precision value is 0.988, and the classification time is 61.9 ms. The experimental results show that the PLA model performs better than all of the comparison methods in terms of accuracy, with an improvement of more than 0.5%. Furthermore, our proposed model demonstrates significant advantages over the comparison methods regarding time and memory.

Electronic Medical Records Using Blockchain Technology

Blockchain technology has emerged as a crucial tool for ensuring security and reliability in various domains, particularly in healthcare. In this study, we utilize blockchain to establish an append-only chain of transaction blocks, ensuring the integrity and security of patient medical records. By employing blockchain, we aim to safeguard patient data, grant specific clinicians’ access to medical records, and ensure user privacy. The doctor will only receive prescription information after the patient has granted access, ensuring comprehensive protection for both parties. Consensus mechanisms within the blockchain guarantee consistency among blocks and require agreement from existing nodes before adding new transactions. Traditional healthcare systems often result in delays in data exchange and strict restrictions on access due to concerns about sensitive data leakage. By integrating blockchain technology into healthcare records and data, this article seeks to enhance data sharing while mitigating the risks of data tampering and security breaches.

A novel blockchain’s private key generation mechanism based on facial biometrics and physical unclonable function

Blockchain technology is widely used in the field of digital currency because of its non-tamperability, traceability, and decentralization. Blockchain’s private key is usually used to prove the ownership of the cryptocurrency. However, this private key managed by the blockchain wallet faces the challenge of secure storage. Once the private key is leaked or stolen, the user’s digital assets will be permanently lost. To solve the storage issue of the private key, we propose a novel approach based on facial biometrics and a physical unclonable function (PUF) device to generate a secure blockchain’s private key. Firstly, to protect user anonymity and enhance the security of the private key, a user’s facial biometrics is bound with a device’s PUF fingerprint to generate the trusted private keys online without being stored in a third-party server or an external device. Secondly, to prevent the leakage of sensitive data, we utilize the correctness and perfectness of secret sharing to protect the helper data to prevent attackers from obtaining sensitive information about the fusion template. Thirdly, we give the formal security proof of our proposed scheme and conduct the informal security analysis. The experiment results demonstrate our scheme achieves a better EER (Equal Error Rate) of 2.02% in terms of accuracy and takes about 1008ms to generate a private key in terms of efficiency. Moreover, our scheme can resist various attacks such as password guessing, stolen mobile device, user impersonation, physical and cloning, and information leakage attacks. Finally, we develop a blockchain wallet prototype without modifying the blockchain protocol to achieve transfer transactions for demonstrating the usability and security of our proposed approach in a real-world scenario.

An Efficient Blockchain Assisted Reputation Aware Decentralized Federated Learning Framework

Because of the widespread presence and ease of access to the internet, edge devices are the perfect candidates for providing quality training on a variety of applications. However, their participation is restrained due to potential leakage of sensitive and private data. Federated learning targets to address these issues by bringing the model to the device and keeping the data in place. Still, it suffers from inherent security issues such as malicious participation and unfair contribution. The central server may become a bottleneck as well as induce biased aggregation and incentives. This article proposes a blockchain assisted federated learning framework, which fosters honest participation with reduced overheads, facilitating fair contribution-based weighted incentivization. A new consensus mechanism named PoIS (Proof of Interpretation and Selection) is proposed based on honest clients’ contributions. PoIS uses model interpretation techniques for evaluating and calculating individual contributions. The aggregation of feature attributions in PoIS, is able to detect the adversaries, and the label-wise aggregation of attributions across the participants helps to define the prominent contributors. Further, we devise a credit function based on the contribution, relevance as well as the past performance for calculating incentives. Extensive experiments have been carried out for the proposed architecture with different settings, models, and datasets, to verify our claim. It successfully restricts the attack to less than 5%, and selects the prominent (top-k) contributors. Theoretical analysis provides the guarantee for byzantine-robust aggregation, in a malicious setting.

Secure Internet of Vehicles (IoV) With Decentralized Consensus Blockchain Mechanism

The Internet of vehicles (IoV) is increasingly being used to realize the vision of intelligent transportation systems with the rapid development of computation and communication technologies. However, numerous IoV applications rely on a central unit for storing and processing information and mediators for wireless transmission. This can lead to the leakage of sensitive data and high costs and delays. To address these issues and improve the efficiency of data storage, processing, and sharing in the IoV, we propose a vehicle-based secure blockchain consensus (VBSBC) algorithm. Our VBSBC algorithm overcomes the limitations and drawbacks of state-of-the-art approaches by leveraging blockchain technology and a consensus algorithm to ensure secure communication between vehicles. In addition, the algorithm includes an authentication process and a key distributing and request process, illustrated during vehicles' movement between different zones. In simulation results, our proposed VBSBC algorithm demonstrated high performance compared to existing approaches in terms of authentication delay, key processing time, attack detection rate, throughput, and packet loss rate.