Leakage Of Sensitive Information Research Articles

An edge cloud and Fibonacci-Diffie-Hellman encryption scheme for secure printer data transmission.

Network printers face increasing security threats from network attacks that can lead to sensitive information leakage and data tampering. To address these risks, we propose a novel Fibonacci-Diffie-Hellman (FIB-DH) encryption scheme using edge cloud collaboration. Our approach utilizes properties of third-order Fibonacci matrices combined with the Diffie-Hellman key exchange to encrypt printer data transmissions. The encrypted data is transmitted via edge cloud servers and verified by the receiver using inverse Fibonacci transforms. Our experiments demonstrate that the FIB-DH scheme can effectively improve printer data transmission security against common attacks compared to conventional methods. The results show reduced vulnerabilities to leakage and tampering attacks in our approach. This work provides an innovative application of cryptographic techniques to strengthen security for network printer communications.

Cloud-Edge Collaboration-Based Local Voltage Control for DGs With Privacy Preservation

The increased distributed generators (DGs) have exacerbated voltage violations in active distribution networks (ADNs). Local reactive power control of DG inverters can realize a fast response to frequent voltage fluctuations. However, commonly used model-based voltage control depends upon accurate network parameters and entire ADN data, which may cause the sensitive information leakage of ADN and DG behaviors in practical operation. In this article, a cloud-edge collaboration-based local voltage control strategy for DGs is proposed with privacy preservation. First, a local voltage control framework is established based on cloud-edge collaboration, in which a surrogate model is built based on the graph convolutional neural networks to estimate the ADN voltages. By transferring the surrogate model, the edge side can obtain the exact voltage estimation in the local curve tuning process without the authority of the whole ADN data, preserving the network parameters of ADN. Then, the interarea coordination based on federated learning is proposed to realize the parameter updating of DG control curves, which can achieve better voltage control performance. By updating surrogate submodels based on private data distributed across multiple edge devices, federated learning can effectively preserve DG behaviors. Finally, the effectiveness and adaptability of the proposed control strategy are validated using the modified IEEE 33-node system. The proposed local DG control strategy can effectively cope with voltage problems and enhance the adaptability to variations in practical operation states while considering privacy preservation.

LEAF: Let’s Efficiently Make Adaptive Forwarding in Payment Channel Networks

Blockchain technology is widely applicable to modern payment systems but has inherent throughput limitations. Off-chain networks are proposed to solve scalability issues, which allows parties to efficiently perform micropayments without committing all of the payments to the blockchain. Off-chain payments with security and privacy protection requirements use the smart contract to ensure security and reduce the risk of sensitive information leakage. Although off-chain payments avoid expensive on-chain operations, it raises many concerns, such as the capacity limitation of payment channels and highly dynamic channel status, lowering the throughput of payment channel networks (PCNs). In our work, we explore the path overlap issues in PCNs and propose a decentralized payment routing scheme to improve the network throughput and reduce the redundant traffic overhead of PCNs, thereby guaranteeing efficient payments. The simulation results indicate that the proposed routing algorithm can achieve higher throughput than other routing schemes while guaranteeing short payment times.

Privacy-Preserving Distributed Economic Dispatch of Microgrids: A Dynamic Quantization-Based Consensus Scheme With Homomorphic Encryption

This paper is concerned with the privacy-preserving distributed economic dispatch problem (ED) of microgrids. A homomorphically encrypted consensus algorithm is developed in the absence of a third party to achieve optimal power distribution with the least cost while preventing sensitive information leakage during the entire communication process. For ease of data encryption, a novel estimator-like dynamic quantizer is first constructed, where the information to be transmitted is converted into a series of finite-level codewords. Then, a sufficient condition is derived, taking advantage of mathematical induction and the properties of matrix norms, to ensure that the quantization output is unsaturated and exact consensus is reached. Furthermore, by means of the additive homomorphic property of the Paillier algorithm to embed secrecy in pairwise interaction dynamics, the confidential communication strategy is adopted to ensure that the distributed algorithm converges to the optimal value without disclosing private or sensitive state information of agents. Finally, case studies are provided to illustrate the feasibility and validity of the adopted privacy-preserving ED scheme in IEEE 39-bus power systems.

Federated learning for smart cities: A comprehensive survey

With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big data, fog computing, and edge computing, smart city applications have suffered from issues, such as leakage of confidential and sensitive information. To envision smart cities, it will be necessary to integrate federated learning (FL) with smart city applications. FL integration with smart city applications can provide privacy preservation and sensitive information protection. In this paper, we present a comprehensive overview of the current and future developments of FL for smart cities. Furthermore, we highlight the societal, industrial, and technological trends driving FL for smart cities. Then, we discuss the concept of FL for smart cities, and numerous FL integrated smart city applications, including smart transportation systems, smart healthcare, smart grid, smart governance, smart disaster management, smart industries, and UAVs for smart city monitoring, as well as alternative solutions and research enhancements for the future. Finally, we outline and analyze various research challenges and prospects for the development of FL for smart cities.

A Gate-Level Information Leakage Detection Framework of Sequential Circuit Using Z3

Hardware intellectual property (IP) cores from untrusted vendors are widely used, raising security concerns for system designers. Although formal methods provide powerful solutions for detecting malicious behaviors in hardware, the participation of manual work prevents the methods from reaching practical applications. For example, Information Flow Tracking (IFT) represents a powerful approach to preventing leakage of sensitive information. However, existing IFT solutions either introduce hardware overheads or lack practical automatic working procedures, especially for hardware sequential logic. To alleviate these challenges, we propose a framework that fully automates information leakage detection at the gate level of hardware. This framework introduces Z3, an SMT solver, to automatically check the violation of confidentiality. On the other hand, an automatic tool is developed to remove the manual workload further. In this tool, the gate level hardware is converted to the formal model firstly, and the integrity of the model is assessed. Along with the model converting step, the property for leakage detection is generated as well. The proposed solution is tested on 25 gate-level netlist benchmarks, where sequential designs are included to validate the effectiveness. As a result, Trojans leaking information from circuit outputs can be automatically detected. The measured time consumption of the entire working procedure validates the efficiency of the proposed approach.

Vulnerability Analysis of Smart Contract for Blockchain-Based IoT Applications: A Machine Learning Approach

With the emergence of Blockchain-based Internet of Things (BIoT) applications, smart contracts have become one of the most appealing aspects because they reduce the cost and complexity of distributed administration. However, the immaturity of smart contracts may result in significant financial losses or the leakage of sensitive information. This article first investigates the taxonomy of security issues associated with smart contracts considering BIoT scenarios. To address these security concerns and overcome the limitations of existing methods, a tree-based machine learning vulnerability detection (TMLVD) method is proposed to perform the vulnerability analysis of smart contracts. TMLVD feeds the intermediate representations of smart contracts derived from abstract syntax trees (AST) into a tree-based training network for building the prediction model. Multidimensional features are captured by this model to identify smart contracts as vulnerable. The detection phase can be implemented quickly with limited computing resources and the accuracy of the detection results is guaranteed. The experimental evaluation demonstrated the effectiveness and efficiency of TMLVD on a data set comprised of Ethereum smart contracts.

Social Networks Privacy Preservation: A Novel Framework

The development of several popular social networks and the publication of social networks’ data have led to the risk of leakage of sensitive and confidential information of individuals. This requires the preservation of privacy before the publication of a user’s data available from his Online Social Network (OSN) presence. Numerous algorithms have been proposed in the area of preserving the privacy of social network users’ information such as K-anonymity and L-diversity. Previous work has shown good results based on the concept of adding edges and noise nodes for achieving K-anonymity and L-diversity. K-anonymization techniques are able to prevent identity disclosure of users but are not sufficient to prevent the disclosure of sensitive information of users. In this direction, a number of techniques for preserving the sensitive information of social network users have been proposed. Although these techniques have shown reasonably good results to achieve anonymity, but they also lead to a substantial change in the original structure of the OSNs. In this article, the problems of preventing sensitive attribute disclosure and reducing the noisy nodes have been addressed by perturbing the sensitive attributes. Existing research uses L-diversity for preventing sensitive attribute disclosure resulting in skewness and similarity attacks. We have addressed the skewness attacks by removing the duplicate noisy nodes from the final dataset to be published for stakeholders by the OSN service providers. All the information of duplicate nodes has been stored in a table named Reference Attribute Table (RAT). This table will be accessible only to the service providers for the purpose of de-anonymizing the data of users. The proposed technique has been extensively evaluated using five metrics viz. APL, ACSPL, RRTI, number of noisy nodes, and information loss using four real-time datasets collected for OSNs namely CORA, ARNET, DBLP, and Twitter. Results of evaluation parameters viz. APL and RRTI show that there is less change in the structure of datasets after anonymization. Results of ACSPL show that our proposed technique is able to preserve sensitive attributes in the datasets. The maximum number of noisy nodes amongst all four datasets is 5.4% and the maximum information loss is 2.2%. Evaluation results make it evident that our proposed technique ensures privacy preservation with less loss of information and thus preserving the utility of published data.

Analysis of vulnerability fixing process in the presence of incorrect patches

Software vulnerabilities or security breaches can have consequences like leakage of sensitive information and malware execution, which are critical to network security. Consequently, eliminating security loopholes and vulnerabilities is imperative for the system administrator to counteract security attacks. Software should be thoroughly reviewed before it is released to uncover these security invasions. However, it is not feasible to identify and overcome all software failures during software testing due to external instances of software development, implementation costs, execution time, and unanticipated modifications to the specification. Security patching is a viable solution for such software systems to prevent attackers from exploiting existing vulnerabilities. Even after patch distribution and installation, it is crucial to determine whether the patch has effectively eliminated the vulnerability. Incorrect patches may lead to new security bugs, which may be malicious and disastrous for developing businesses and users. The present research aims to model the trend of patched vulnerabilities methodically by incorporating the generation of new vulnerabilities due to unsuccessful updations and encompassed bug fixes. The proposed analytical model is validated on the vulnerability databases obtained from the Common Vulnerabilities and Exposures repository. The empirical analysis yields that the present research has better forecasting efficacy than the benchmark studies.

The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition.

Concerns regarding inappropriate leakage of sensitive personal information as well as unauthorized data use are increasing with the growth of genomic data repositories. Therefore, privacy and security of genomic data have become increasingly important and need to be studied. With many proposed protection techniques, their applicability in support of biomedical research should be well understood. For this purpose, we have organized a community effort in the past 8 years through the integrating data for analysis, anonymization and sharing consortium to address this practical challenge. In this article, we summarize our experience from these competitions, report lessons learned from the events in 2020/2021 as examples, and discuss potential future research directions in this emerging field.

Improving malicious email detection through novel designated deep-learning architectures utilizing entire email

In today’s email dependent world, cyber criminals often target organizations using a variety of social engineering techniques and specially crafted malicious emails. When successful, such attacks can result in significant harm to physical and digital systems and assets, the leakage of sensitive information, reputation damage, and financial loss. Despite the plethora of studies on the detection of phishing attacks and malicious links in emails, there are no solutions capable of effectively, quickly, and accurately coping with more complex email-based attacks, such as malicious email attachments. This paper presents the first fully automated malicious email detection framework using deep ensemble learning to analyze all email segments (body, header, and attachments); this eliminates the need for human expert intervention for feature engineering. In this paper, we also demonstrate how an ensemble framework of deep learning classifiers each of which are trained on specific portions of an email (thereby independently utilizing the entire email) can generalize better than popular email analysis methods that analyze just a specific portion of the email for analysis. The proposed framework is evaluated comprehensively and with an AUC of 0.993, the proposed framework’s results surpass state-of-the-art malicious email detection methods, including human expert feature-based machine learning models by a TPR of 5%.

Guided Data Augmentation Scheme Combined with Adaptive Evolutionary Algorithm for Hardware Trojan Detection to Enhance Communication Security

The security of the RS232 module plays a pivotal role in ensuring secure communication. Hardware Trojans can be used to gain root shell access to the RS232 modules to leak sensitive information or perform other malicious actions. As a result, it is crucial to detect the presence of hardware Trojans effectively. The work proposes a guided data augmentation scheme that facilitates enhanced synthetic data generation without any false positives. The proposed scheme uses instance hardness based under sampling to effectively remove data with less information and ADASYN upsampling algorithm to produce more realistic synthetic data. The work thus effectively utilizes the information present in majority and minority data instances effectively. In order to facilitate accurate Trojan detection, hyperparameters of the XGBoost model are optimized using genetic algorithm. The model produces zeros false positives and false negatives for all the test circuits.

Optimal design of supply chain inventory information forecasting and control system in cloud environment for biomedical instruments

With the rapid development of Internet technology, the network information data is exploding, and society has entered the era of cloud data. Cloud data provides a lot of data support for people's life and work, but because of its large number, multifarious types and huge value, there are some problems such as private data leakage and abuse of sensitive information. Stocking of biomedical material at one place only, can cause delay in sending important lifesaving material to needy patients. On the one hand, it is an important content of engineering scheduling theory and method; On the other hand, in medical field, a lot of materials are usually consumed to protect the life of patients. Biomedical informatics and computer vision techniques have been combined in a variety of inter-multidisciplinary disciplines during the past few decades. We are all aware that inadequate or insufficient cloud access management and controls can expose a corporation to a range of issues. The work that follows offers intelligent supply chain, an upgraded service management control telephone network architecture. If we can acquire according to the patient demands and retain the ideal inventory such that the patient requirement and interest can be safe, the overall cost will be lowered greatly. The experimental results show that R1 orders 31 times, and the total cost is 1,259,520 CNY. R2 ordered 24 times, with a total cost of 982,034 CNY; R3 orders 22 times, and the total cost is 990,146 CNY. Finally, the optimal solution of the total cost after schedule optimization is 3,231,700 CNY. It is proved that the optimal cost finally obtained by forecasting supply chain inventory information based on cloud data environment is also more practical for engineering practice.

LBS user location privacy protection scheme based on trajectory similarity

During the data set input or output, or the data set itself adds noise to enable data distortion to effectively reduce the risk of user privacy leakage. However, in the conventional method, the added noise may cause data distortion, thereby appealed against it. However, the amount of noise is too small and cannot meet the effect of privacy protection. Therefore, we propose a LBS user location privacy protection scheme based on trajectory similarity (DPTS). With double privacy protection without reducing the efficiency of algorithms, it does not cause data distortion to provide more reliable privacy protection. The main contributions of this article include: (1) In the process of collecting and publishing the location data, introduce into the privacy protection method, (2) The differential privacy algorithm based on the trajectory prefix tree is superimposed on the basis of the false position replacement algorithm based on the trajectory similarity, (3) Propose LBS-based Difference Privacy Protection Algorithm. In the algorithm, We reach the purpose of protecting user personal privacy by replace the original trajectory into a fake track trace that is the lowest degree of similarity in the interval. Then establish a prefix tree and add noise to the positional frequency. It is in order to further protect the sensitive location information, double protection in the trajectory data set, and the degree of privacy protection is improved. Simulation experiment results show that the proposed algorithm is effective. The algorithm can suppress the distortion rate of data while improving the amount of noise, and in improving the algorithm operation efficiency, it reduces the risk of leakage of sensitive position information.

Predicting sensitive information leakage in IoT applications using flows-aware machine learning approach

This paper presents an approach for identification of vulnerable IoT applications. The approach focuses on a category of vulnerabilities that leads to sensitive information leakage which can be identified by using taint flow analysis. Tainted flows vulnerability is very much impacted by the structure of the program and the order of the statements in the code, designing an approach to detect such vulnerability needs to take into consideration such information in order to provide precise results. In this paper, we propose and develop an approach, FlowsMiner, that mines features from the code related to program structure such as control statements and methods, in addition to program’s statement order. FlowsMiner, generates features in the form of tainted flows. We developed, Flows2Vec, a tool that transform the features recovered by FlowsMiner into vectors, which are then used to aid the process of machine learning by providing a flow’s aware model building process. The resulting model is capable of accurately classify applications as vulnerable if the vulnerability is exhibited by changes in the order of statements in source code. When compared to a base Bag of Words (BoW) approach, the experiments show that the proposed approach has improved the AUC of the prediction models for all algorithms and the best case for Corpus1 dataset is improved from 0.91 to 0.94 and for Corpus2 from 0.56 to 0.96.

Performance Analysis Of Stego Image Calibration With Usage Of Denoising Autoencoders

Methods for early detection of sensitive information leakage by data transmission in open (public) communication systems have been of special interest. Reliable detection of modified (stego) cover files, like digital images, requires usage of computation-intensive methods of statistical steganalysis, namely covering rich models and deep convolutional neural networks. Necessity of fine- tuning parameters of such methods to minimize detection accuracy for each embedding methods has made fast re- train of stegdetectors in real cases impossible. Therefore, development of low-complexity methods for detection of weak alterations of cover image parameters under limited prior information about used embedding methods has been required. For solving this task, we have proposed to use special architectures of artificial neural networks, such as denoising autoencoder. Ability of such networks to estimate parameters of original (cover) image from the noisy ones under limited prior information about introduced alterations has made them an attractive alternative to state- of-the-art solutions. The results of performance evaluation for shallow denoising autoencoders showed increasing of detection accuracy (up to 0.1 for Matthews correlation coefficient) in comparison with the state-of-the-art stegdetectors by preserving low-computation complexity of network retraining.

AI Based Self Learning Intelligent Information Leak Protection System for TI Companies using LSTM

Preventing the leak of sensitive information, also popularly known as data leak or data loss to an unauthorized recipient, is the primary goal of an organization's information security system. A data leak can occur through multiple channels. While it may not always be possible to prevent it entirely, measures can be taken to minimize the possibility of the occurrence. Like all other financial institutions, TI companies collect sensitive personal information of their customers for business purposes. This information is often categorized into three primary types; NPI, PII, and PI are the designated types in the descending order of sensitivity. The detection of sensitive documents and redaction of sensitive information is required if it is needed to be shared. Inspection of such digital documents to find any sensitive information is by far a human-driven process, and thus time-consuming and costly. An and costly. An intelligent and robust system is required where the content is analysed by state-of-the-art data mining, statistical and machine learning techniques from various data dimensions. An AI based self-learning Intelligent Information Leak Protection System using LSTM is proposed in the project that mines and extracts information and categorizes the document images, to SD or NSD, based on the presence of NPI and PII semantic signatures without any explicit rule configuration. The system is designed to be used proactively as an early warning system to tag the SD images while resting in the data store. It can also act as a real-time checkpoint for the information loss by the documents in transit or use. The proposed model prescribes an information loss protection mechanism using a binary classifier based on the state-of-the-art LSTM technique within the paradigm of Artificial Intelligence.

Research on Access Control Scheme of System Wide Information Management Based on Attribute Association

System wide information management (SWIM) involves civil aviation system control, intelligence, alarm, traffic, and other data. These data are transmitted in various forms, making SWIM system vulnerable to sensitive information leakage, data tampering, denial, and other security threats. In this article, an attribute-based air traffic management (ATM) information access control scheme is proposed to solve the security threat of SWIM. An improved extensible access control markup language (XACML) authorization model is established, combining linear secret sharing scheme (LSSS) matrix structure and monotone span program (MSP); an attribute association algorithm is designed to establish the attribute association relationship between services and users. Experimental results show that the attribute association algorithm improves the time complexity, but the algorithm can support richer policy representation capability, and the proposed ATM information access control scheme is more efficient and can effectively reduce the space cost. This scheme can achieve more fine-grained and flexible access control.

Privacy-preserving policy evaluation in multi-party access control

Recent years have seen an increasing popularity of online collaborative systems like social networks and web-based collaboration platforms. Collaborative systems typically offer their users a digital environment in which they can work together and share resources and information. These resources and information might be sensitive and, thus, they should be protected from unauthorized accesses. Multi-party access control is emerging as a new paradigm for the protection of co-owned and co-managed resources, where the policies of all users involved in the management of a resource should be accounted for collaborative decision making. Existing approaches, however, only focus on the jointly protection of resources and do not address the protection of the individual user policies themselves, whose disclosure might leak sensitive information. In this work, we propose a privacy-preserving mechanism for the evaluation of multi-party access control policies, which preserves the confidentiality of user policies while remaining capable of making collaborative decisions. To this end, we design secure computation protocols for the evaluation of policies in protected form against an access query and realize such protocols using two privacy-preserving techniques, namely Homomorphic Encryption and Secure Functional Evaluation. We show the practical feasibility of our mechanism in terms of computation and communication costs through an experimental evaluation.

An efficient and access policy-hiding keyword search and data sharing scheme in cloud-assisted IoT

By leveraging the cloud computing paradigm, the cloud-assisted Internet of Things (IoT) is able to offer the massive storage and highly available computation services for end-users. When the data collected from IoT devices is endlessly gathering to the cloud center, the data security become new challenges. Encrypting data is an effective measure to guarantee data confidentiality. However, traditional encryption techniques make the data searching and access control inoperative. Current attribute-based keyword search (ABKS) techniques provide a feasibility to achieve data searching and access control over encrypted data simultaneously. However, existing ABKS schemes leak sensitive information via clear access policy and may be unsuitable for certain IoT applications, where the access policies in data might contain private information of data owners such as the security number or the residential area of a resident in smart grids. In this work, we design an efficient and policy-hiding attribute-based keyword search and data sharing scheme (PH-ABKS-DS) in cloud-assisted IoT. To the best of our knowledge, this construction is the first PH-ABKS-DS with practical efficiency that is constructed on prime order group. We provide detailed correctness and security analyses for PH-ABKS-DS. Extensive experiments demonstrate that our proposed PH-ABKS-DS is correct and practical.