Information Leakage Research Articles

Application and Optimization of Multi-Factor Authentication and Biometric Technology in Power Grid Energy Network Access Control

With the sudden development in electronic and network technology, the power grid has emerged in several evolved nations and other areas with faster development. Energy storage can enhance the stability, resiliency and efficiency of the electric smart grid. The power grid transfers energy via bidirectional control of energy and information flow. In the power grid, a smart meter is an important component that is deployed on the user side to determine the consumption of energy regularly. However, the data about the usage of electricity leads to the leakage of the private information of the user, which threatens the privacy of the user. The attackers can change the in-transit message induce wrong information and sometimes cause disastrous action. Hence, it is important to assure data security and privacy of users by conducting authentication in power grid communication. An optimization technique named Puzzle Optimization Algorithm (POA) is designed for the efficient privacy-preserving aggregation approach with multi-factor authentication and a biometric scheme. Also, optimal key-based Elliptic Curve Cryptography (ECC) is used to encrypt the sensitive data that are securely stored. Authentication offers high security and texture analysis. In the registration phase, the privacy information of the person with their biometrics is the important feature point of the individual unique identification. Additionally, the designed approach is compared with other existing optimizations to analyse the functionality of the implemented technique. The simulation outcome defined that the suggested technique enhances user privacy and information security than other standard approaches.

A blockchain-assisted lightweight UAV network authentication mechanism via covert communication

The increasing importance of terminal privacy in the Unmanned Aerial Vehicle (UAV) network has led to a growing recognition of the crucial role of authentication technology in UAV network security. However, traditional authentication approaches are vulnerable due to the transmission of identity information between UAVs and cryptographic paradigm management centers over a public channel. These vulnerabilities include brute-force attacks, single point of failure, and information leakage. Blockchain, as a decentralized distributed ledger with blockchain storage, tamper-proof, secure, and trustworthy features, can solve problems such as single-point-of-failure and trust issues, while the hidden communication in the physical layer can effectively resist information leakage and violent attacks. In this paper, we propose a lightweight UAV network authentication mechanism that leverages blockchain and covert communication, where the identity information is transmitted as covert tags carried by normal modulated signals. In addition, a weight-based Practical Byzantine Fault-Tolerant (wPBFT) consensus protocol is devised, where the weights are determined by the channel states of UAVs and the outcomes of past authentication scenarios. Simulation results demonstrate that the proposed mechanism outperforms traditional benchmarks in terms of security and robustness, particularly under conditions of low Signal-to-Noise Ratio (SNR) and short tag length.

Information leakage prior to market switches and the importance of Nominated Advisers

This study tests the information leakage hypothesis prior to the public announcement of firms switching between the Alternative Investment Market (AIM) and the Main Market (MM) in the UK. We find significant abnormal stock returns 60 trading days prior to the announcement of these switches. The results are robust after controlling for switching anticipation, rumors, other major corporate announcements, and firm performance one year prior to the switch. We also show that having a reputable Nominated Adviser (Nomad) significantly moderates the abnormal stock returns prior to market switches. However, this effect does not hold when Nomads also act as brokers in firms that switch markets. Overall, these findings provide novel evidence about abnormal stock returns prior to the announcement of market switches in the UK and the role of Nomads. As such, we shed light on the significance and the limits of decentralized regulation on informed trading activity.

Revisiting frequency-smoothing encryption: new security definitions and efficient construction

Deterministic encryption (DET) allows for fast retrieval of encrypted information, but it would cause significant leakage of frequency information of the underlying data, which results in an array of inference attacks. Simply replacing DET with fully randomized encryption is often undesirable in the scenario of an encrypted database since it incurs a large overhead in query and storage. Frequency Smoothing Encryption (FSE) is a practical encryption scheme to protect frequency information. Current FSE constructions still fall short of efficiency and a reasonable security definition. We revisit FSE and propose two security definitions from both theoretical and practical perspectives. Furthermore, we adopt a novel partitioning strategy to construct a new FSE scheme to improve performance. Experimental results show that compared with others, our scheme achieves excellent query performance while attaining security against inference attacks.

Achieving zero-outage secrecy in short packet cooperative networks

Zero-outage secrecy in short packet is critical in many applications ranging from civilians to military as it guarantees no information leakage to eavesdropper with one hundred percent for any circumstance. This paper aims to consider a novel transmission strategy to achieve zero-outage secrecy in short packet cooperative networks. The zero-outage capacity is derived to guarantee secrecy such that eavesdropper's intercept probability is always at 0.5. The results show that the zero-outage secrecy throughput increases with increasing signal-to-noise ratio (SNR) regardless how powerful eavesdropper is. This indicates the efficient implement of the proposed scheme in practical scenarios when eavesdropper is located close to transmitter or has unlimited number of antenna. The results also show that there exists an optimum number of antenna to maximize energy efficiency.

An adversarial network reinforcement method for power data time series generation based on knowledge transfer and Rayleigh differential privacy

Abstract In order to solve the problem of sensitive information leakage of power data in machine learning and artificial intelligence processing, this paper proposes a method of civic-text data reinforcement based on time series generation adversarial network, which combines knowledge transfer and Rényi differential privacy protection framework to balance privacy and availability, while improving the accuracy of the model. First, the generative adversarial network generates training data that is similar to the real data but secure through adversarial learning, ensuring that even if the data is stolen, the original information cannot be inferred. Secondly, through the integration of recurrent neural network training and autoencoder, the dynamic and static characteristics of the power load data can be effectively captured, and the high dimension of the learning space can be reduced. In addition, knowledge distillation technology is used to guide the generator of training student models through a powerful teacher network of knowledge transfer, improving the diversity and quality of the generated data. Finally, from the four aspects of privacy, data similarity and diversity, and practicability, the practical application value and potential of the framework for electricity consumption data are comprehensively evaluated.

A cancellable iris template protection scheme based on inverse merger and Bloom filter

Iris recognition has found extensive applications in real-world situations and financial contexts. However, Iris template protection schemes are highly vulnerable to well-planned attacks that can lead to the leakage of personal information. Once biological information is compromised, this loss is irreversible for the individual. Cancelable protection schemes for iris templates based on the Bloom filter have substantial attention in the field of iris biometrics. Nevertheless, Bloom filter-based template protection schemes face specific security challenges. Therefore, it is crucial to propose a method to protect iris templates that is both secure and efficient. To address irreversible limitations in security analysis, we propose a template protection scheme, a cancelable iris biometric protection scheme based on inverse merger and Bloom filter. The primary idea of the proposed scheme is to perform an inverse merger operation on the acquired codewords before mapping the iris templates to the Bloom filter specifically. Through a comparison of the sizes between the original templates and their inverted counterparts, the template with the smaller size is chosen as the definitive result, subsequently being mapped into the Bloom filter. Our proposed scheme exhibits significant advancements in accuracy across multiple datasets, as evidenced by empirical validations. In the optimal case, our model achieves an excellent performance of 98.04% in terms of GAR, while achieving a significant reduction of 0.51% in terms of EER. Furthermore, a comparative analysis with existing iris template protection methods is performed to evaluate its relative effectiveness in resisting the attack of averaging the columns of a block. The results demonstrate that the scheme exhibits robust resistance to such attacks. The experimental analysis demonstrated that the scheme provided a good balance between accuracy and safety.

Civil liability of a medical organization for personal data leakage

Relevance. When considering claims for leakage of information constituting the patient's personal data and his medical confidentiality, the courts give a different assessment of the grounds for civil liability of medical organizations. The article reveals the established approaches to assessing the guilt of a medical organization that violated the confidentiality of personal information of citizens who applied for medical help.The purpose of the study is to supplement theoretical provisions aimed at deepening scientific knowledge about the civil liability of medical organizations for the unlawful dissemination and (or) use of patient's personal data, including information related to medical confidentiality; development of proposals to eliminate the imbalance of the patient's personal data confidentiality regime and his right to legal assistance.Objectives: analyze civil legislation, legislation on personal data, the positions of the Constitutional and Supreme Courts of the Russian Federation regarding the protection of patient personal data, decisions of courts of general jurisdiction on claims of liability of medical organizations for unlawful actions with personal data that resulted in the leakage of personal information about patients; to study and update the proposals available in theory aimed at uniform law enforcement in cases of this category.Methodology. The study was carried out through methods of induction and deduction, analysis and synthesis, formal-legal, system-analytical and formal-logical methods, as well as a method for generalizing judicial practice. The results are reflected in the formulated proposals aimed at improving the judicial protection of personal data of patients of medical organizations, and the uniformity of judicial practice in terms of the responsibility of medical organizations in cases of this category.Conclusion. In cases of responsibility of medical organizations for the leakage of personal data of patients, uniform law enforcement did not work out. The right of a lawyer to access the personal data of persons who received medical assistance did not receive an unambiguous assessment by judicial practice. To eliminate the existing gaps in the protection of the intangible benefits of the patient under consideration, the need to exclude their personal data from the confidentiality regime for the administration of justice and in the provision of qualified legal assistance under the contract is argued.

Large language models privacy and security

The advancement of large language models (LLMs) has yielded significant advancements across various domains. Nevertheless, this progress has also raised crucial concerns regarding privacy and security. The paper does a comprehensive literature study to thoroughly examine the fundamental principles of LLM. It also provides a detailed examination of the characteristics and application fields of various LLMs, with a particular focus on Transformer. Furthermore, this study places emphasis on the examination of privacy concerns that may emerge in the context of LLM's handling of personal and sensitive data. It also explores the potential hazards associated with information leakage and misuse, as well as the existing privacy safeguards and the obstacles encountered in their implementation. Overall, LLM has made significant advancements in technology. However, it is imperative to acknowledge the importance of doing research on safeguarding privacy and enhancing security. These aspects are vital for guaranteeing the sustained development and public confidence in LLM technology.

Analysis of problems and countermeasures in computer network engineering security

This paper analyzes the existing security issues in computer network engineering and proposes a series of countermeasures. To address the problems of hacker attacks and information leakage, it is recommended to strengthen network monitoring and the application of encryption technology. For the issues of malware and virus dissemination, it is advised to regularly update antivirus software and enhance employee security awareness training. Regarding network data leakage and privacy breaches, the use of secure transmission protocols and multi-factor authentication technology is advocated. For network security management and maintenance issues, establishing a sound security management system and emergency response plans is recommended to ensure the stable operation of network systems. Through the implementation of the above measures, the security level of computer network engineering can be effectively improved, ensuring the normal operation of network systems and the security of information.

Spurious correlations in surface-based functional brain imaging.

The study of functional MRI data is increasingly performed after mapping from volumetric voxels to surface vertices. Processing pipelines commonly used to achieve this mapping produce meshes with uneven vertex spacing, with closer neighbours in sulci compared to gyri. Consequently, correlations between the fMRI time series of neighbouring sulcal vertices are stronger than expected. However, the causes, extent, and impacts of this bias are not well understood or widely appreciated. We explain the origins of these biases, and using in-silico models of fMRI data, illustrate how they lead to spurious results. The bias leads to leakage of anatomical cortical folding information into fMRI time series. We show that many common analyses can be affected by this "gyral bias", including test-retest reliability, fingerprinting, functional parcellations, regional homogeneity, and brain-behaviour associations. Finally, we provide recommendations to avoid or remedy this spatial bias.

Research on Personal Information Protection in the Context of Generative Artificial Intelligence

Personal information is the foundation of generative AI, and ChatGPT-like generative AI needs to process a large amount of personal information at various stages such as model training, model generation, and model optimization, which also has a certain impact on traditional personal information protection rules. During the information collection phase, generative AI may fugitive the informed consent rules and infringe on the privacy rights of information subjects. In the information utilization stage, generative AI may impact basic personal information processing rules such as the principle of purpose limitation and the principle of openness and transparency, increasing the risk of personal information leakage. At the information generation stage, generative AI can generate false and discriminatory information. Therefore, in the context of generative AI, personal information protection is faced with the problems of the notification and consent rules being hollowed out, the principle of minimum necessity being voided, and the frequent leakage of personal information. Based on this, it is necessary to promote the transformation of the "personal control center to the risk control center" of the notification and consent rules, promote the risk-based interpretation of the principle of least necessary, and improve the risk-based personal information protection compliance system to solve the problem of personal information protection in the context of generative AI.

Enhancing Security and Privacy in Large Language Model-based Approaches: A Comprehensive Investigation

During the last five years, there have been significant developments in the field of Natural Language Processing including the deployment of advanced large language models such as ChatGPT, Bard and Llama. These large language models are helpful in generating text and designing content and they have several applications in various industries. However, they can memorize and reveal malicious content and personal information from their training dataset which also includes an enormous amount of data from the internet. As a result, it can lead to compromised privacy and security challenges for users who have their personal information available on the internet directly or through third parties. To address this issue, the proposed research work conducts a thorough investigation of these challenges and puts forward a prompt designing-based solution. In this method, we build a customized training dataset to fine-tune a pre-trained model (Llama-2) to produce a harmless response ‘I can’t provide you with this information’ to prompts seeking to extract personal information and malicious content from LLMs. Experimental results reveal that the proposed work achieves an accuracy of 63% with a precision score of 0.706 and a recall score of 0.571. The work ensures almost no leakage of private information and strengthens the LLM model against extraction attacks.

Time‐Dependent Wrinkle Pattern Based on Photo‐Controlled Stress Relaxation for Multi‐Level Information Encryption and Information Camouflage

AbstractStimuli‐responsive surfaces have garnered intensive research attention in addressing the challenges of severe information leakage problems, but they exhibit limitations in terms of intricate preparation and compatibility. Herein, the study reports a time‐dependent wrinkled surface controlled by photo‐induced stress relaxation of azobenzene‐ containing crosslinked polymer network comprised of furan‐containing polymer (PEA‐Fu) and maleimide‐substituted azobenzene (AZO‐2MI) as skin layer. Two kinds of wrinkled surfaces induced by two crosslinked networks (thermal‐ and UV‐induced) can be prepared by Diels‐Alder reaction and UV‐induced radical coupling reaction, exhibiting distinct amplitude decrease rate resulted from different stress relaxation rate induced by photo‐isomerization of azobenzene. By UV preprogramming with photomasks, various time‐dependent wrinkle patterns are obtained and can be identified at specific times under 450 nm irradiation. The time window for information read out can be preset through different UV preprogramming time and boundary conditions to achieve multi‐level information encryption. Furthermore, the written information can be camouflaged as another “false” information by regional‐selective irradiation under 450 nm irradiation, which offers a new method for information protection. Based on distinctive time‐dependent characteristics and excellent stability, this responsive wrinkled surface offers bright prospects in multi‐level information encryption.

Correlation inference attacks against machine learning models.

Despite machine learning models being widely used today, the relationship between a model and its training dataset is not well understood. We explore correlation inference attacks, whether and when a model leaks information about the correlations between the input variables of its training dataset. We first propose a model-less attack, where an adversary exploits the spherical parameterization of correlation matrices alone to make an informed guess. Second, we propose a model-based attack, where an adversary exploits black-box model access to infer the correlations using minimal and realistic assumptions. Third, we evaluate our attacks against logistic regression and multilayer perceptron models on three tabular datasets and show the models to leak correlations. We lastly show how extracted correlations can be used as building blocks for attribute inference attacks and enable weaker adversaries. Our results raise fundamental questions on what a model does and should remember from its training set.

Securing Machine Learning: Understanding Adversarial Attacks and Bias Mitigation

This paper offers a comprehensive examination of adversarial vulnerabilities in machine learning (ML) models and strategies for mitigating fairness and bias issues. It analyses various adversarial attack vectors encompassing evasion, poisoning, model inversion, exploratory probes, and model stealing, elucidating their potential to compromise model integrity and induce misclassification or information leakage. In response, a range of defence mechanisms including adversarial training, certified defences, feature transformations, and ensemble methods are scrutinized, assessing their effectiveness and limitations in fortifying ML models against adversarial threats. Furthermore, the study explores the nuanced landscape of fairness and bias in ML, addressing societal biases, stereotypes reinforcement, and unfair treatment, proposing mitigation strategies like fairness metrics, bias auditing, de-biasing techniques, and human-in-the-loop approaches to foster fairness, transparency, and ethical AI deployment. This synthesis advocates for interdisciplinary collaboration to build resilient, fair, and trustworthy AI systems amidst the evolving technological paradigm.

Restoring private autism dataset from sanitized database using an optimized key produced from enhanced combined PSO-GWO framework

The timely identification of autism spectrum disorder (ASD) in children is imperative to prevent potential challenges as they grow. When sharing data related to autism for an accurate diagnosis, safeguarding its security and privacy is a paramount concern to fend off unauthorized access, modification, or theft during transmission. Researchers have devised diverse security and privacy models or frameworks, most of which often leverage proprietary algorithms or adapt existing ones to address data leakage. However, conventional anonymization methods, although effective in the sanitization process, proved inadequate for the restoration process. Furthermore, despite numerous scholarly contributions aimed at refining the restoration process, the accuracy of restoration remains notably deficient. Based on the problems identified above, this paper presents a novel approach to data restoration for sanitized sensitive autism datasets with improved performance. In the prior study, we constructed an optimal key for the sanitization process utilizing the proposed Enhanced Combined PSO-GWO framework. This key was implemented to conceal sensitive autism data in the database, thus avoiding information leakage. In this research, the same key was employed during the data restoration process to enhance the accuracy of the original data recovery. Therefore, the study enhanced the restoration process for ASD data's security and privacy by utilizing an optimal key produced via the Enhanced Combined PSO-GWO framework. When compared to existing meta-heuristic algorithms, the simulation results from the autism data restoration experiments demonstrated highly competitive accuracies with 99.90%, 99.60%, 99.50%, 99.25%, and 99.70%, respectively. Among the four types of datasets used, this method outperforms other existing methods on the 30-month autism children dataset, mostly.

Information leakage risk and countermeasures in the age of artificial intelligence - Taking Chatgpt as an example

Information leakage risk and countermeasures in the age of artificial intelligence - Taking Chatgpt as an example

Method for Assessing Noise Quality Based on Entropy Quality Factor

The work analyzes the existing approaches to solving the problem of a comprehensive study of the qualitative characteristics of side electromagnetic emission generators. In addition, methods and means of information leakage through the channels of side electromagnetic emission are considered, which make it possible to evaluate the effectiveness of noise generators. Electromagnetic emission can propagate in almost all environments. Side electromagnetic emission is one of the effective ways to protect computer equipment from information leakage through the channel of side electromagnetic emissions and interference. The masking noise is generated by the noise generator, and the quality factor of the masking noise is the quality factor of the entropy noise. In addition, measurements of masking noise interference using a spectrum analyzer and an oscilloscope are carried out and a method for estimating the entropy noise quality coefficient (ENQC) is proposed. The calculated value of the entropy noise quality factor is compared with the normalized value set for this type of noise generator. The novelty of the work is the development of a new method for assessing the quality of masking noise interference, based on the calculation of the entropy quality coefficient, as well as the development of software for automating the calculation. The study of the quality characteristics of the noise generator (NG) taking into account the design of a mock-up sample of the device, which is a complex work. In turn, based on the results obtained, it is possible to develop specialized automated tools for assessing the quality of masking noise interference.

Secure physical layer transport strategies in mobile edge computing

Abstract In the past, mobile edge computing faced problems such as information leakage and eavesdropping during offloading of computing resources. To bolster the comprehensive security of the mobile edge computing system, a physical layer secure transmission strategy in mobile edge computing is proposed. First, a drone-mobile edge computing system is studied, which consists of M legitimate drones, N user devices, and K eavesdropping drones. Second, a multi-intelligence deep deterministic policy gradient algorithm is used to optimize the flight path, transmit power, and offloading ratio of the legitimate UAVs in combination with a prioritized empirical replay mechanism. Finally, under the premise of guaranteeing offloading, it realizes that the user’s confidential information is not stolen by eavesdroppers, safeguards the overall security of the system, and maximizes the efficiency of security computing. In this paper, the proposed algorithm enhances security transmission efficiency by 50% compared to other benchmark schemes. This demonstrates that the algorithm is effective.