Leakage Of Private Information Research Articles

Efficient and secure privacy protection scheme and consensus mechanism in MEC enabled e-commerce consortium blockchain

The application of blockchain technology to the field of e-commerce has solved many dilemmas, such as low transparency of transactions, hidden risks of data security and high payment costs. Mobile edge computing(MEC) can provide computational power for blockchain, and can meet the demand for high real-time and low latency in e-commerce transaction systems. However, there are still some constraints in the MEC enabled e-commerce consortium blockchain, such as the leakage of user privacy information, low security of consensus algorithm and other security issues. In this paper, we propose a secure transaction model suitable for MEC enabled e-commerce consortium blockchain, aiming to ensure the efficiency of system transaction processing while improving the security of users’ privacy information and transaction data. The model adopts the lightweight Paillier encryption algorithm to protect the security of user privacy information and transaction data to prevent the leakage of user privacy information, and optimizes the security of leader election phase of Raft consensus algorithm by introducing the shamir secret sharing protocol to improve the anti-Byzantine failure capabilities of Raft consensus algorithm. The effectiveness of the scheme proposed in this paper is demonstrated by experimental simulations.

Privacy information protection of medical internet based on double chaos encryption algorithm

In order to avoid the risk of patients’ private information leakage, this paper puts forward a research on the protection of medical Internet private information based on double chaotic encryption algorithm. This paper analyzes the quantification of risk indicators for privacy information protection of medical Internet, establishes the risk quantification structure of health care big data according to the quantitative calculation results, and puts forward the strategy of controlling access to health care big data, configuring the risk level, describing the attributes of the system database, and realizing the privacy information protection of medical Internet under the double chaotic encryption algorithm. The experimental results show that the real identity of patients is protected to a certain extent in the protection of private information of medical internet after applying this method. Moreover, this method has high storage integrity and small storage standard deviation, and the method in this paper can effectively resist network intrusion. Therefore, it shows that this method has a good effect of protecting private information of medical Internet.

Decency or devilment: the impact of blockchain technology on product line design

ABSTRACT Blockchain is a secure database shared across a network of all participants, characterised by immutability, openness, transparency, and traceability. Therefore, blockchain technology is widely used in supply chain management, which can increase consumers' understanding of product information when goods arrive at the consumer end and increase the risk of consumer privacy information leakage. Considering a supply chain system that consists of a manufacturer and a platform, this paper studies the strategic interactions between the platform's adoption of blockchain technology and the manufacturer's product line design based on blockchain's positive and negative impacts on consumers. We examine the driving factors of the platform's use of blockchain technology, which are related to the effect of information transparency and privacy concerns caused by blockchain and the marginal production cost coefficient. In particular, a higher marginal production cost coefficient could make the platform more likely to use blockchain technology. We also find that the practice of blockchain technology may incentivize a manufacturer to extend its product line.

SoK: Secure Human-centered Wireless Sensing

Human-centered wireless sensing (HCWS) aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around him/her. While the sensed information about a human can be used for many good purposes such as enhancing life quality, an adversary can also abuse it to steal private information about the human (e.g., location and person's identity). However, the literature lacks a systematic understanding of the privacy vulnerabilities of wireless sensing and the defenses against them, resulting in the privacy-compromising HCWS design. In this work, we aim to bridge this gap to achieve the vision of secure human-centered wireless sensing. First, we propose a signal processing pipeline to identify private information leakage and further understand the benefits and tradeoffs of wireless sensing-based inference attacks and defenses. Based on this framework, we present the taxonomy of existing inference attacks and defenses. As a result, we can identify the open challenges and gaps in achieving privacy-preserving human-centered wireless sensing in the era of machine learning and further propose directions for future research in this field.

Multi-Home Energy Coordination Scheduling Based on Adaptive Dynamic Programming

Implementing cooperative scheduling of multi-home microgrid energy and reducing the dependence on the main grid has become the focus of microgrid energy management research. This paper proposes a new multi-agent adaptive dynamic programming (MAADP) method for the cooperative control of distributed home energy. Each home is defined as a learning agent that needs to reasonably schedule the energy storage system to meet the respective load demand while accomplishing cooperative scheduling among the individual homes. In addition, an energy clearing center (ECC) is introduced to complete the energy exchange between each microgrid to protect the benefits of all parties. The proposed method adopts the learning strategy of ”centralized learning and decentralized execution”to avoid the leakage of private information. The experimental comparison with the benchmark method verifies that the method can realize the cooperative scheduling of each home and reduce the dependence on the main grid.

Federated Learning-based Information Leakage Risk Detection for Secure Medical Internet of Things

The Medical Internet of Things (MIoT) requires extreme information and communication security, particularly for remote consultation systems. MIoT’s integration of physical and computational components creates a seamless network of medical devices providing high-quality care via continuous monitoring and treatment. However, traditional security methods such as cryptography cannot prevent privacy compromise and information leakage caused by security breaches. To solve this issue, this paper proposes a novel Federated Learning Intrusion Detection System (FLIDS). FLIDS combines Generative Adversarial Network (GAN) and Federated Learning (FL) to detect cyber attacks like Denial of Service (DoS), data modification, and data injection using machine learning. FLIDS shows exceptional performance with over 99% detection accuracy and 1% False Positive Rate (FPR). It saves bandwidth by transmitting 3.8 times fewer bytes compared to central data collection. These results prove FLIDS’ effectiveness in detecting and mitigating security threats in Medical Cyber-Physical Systems (MCPS). The paper recommends scaling up FLIDS to use computing resources from multiple mobile devices for better intrusion detection accuracy and efficiency while reducing the burden on individual devices in MIoT.

A Data Hierarchical Encryption Scheme Based on Attribute Hiding under Multiple Authorization Centers

The data hierarchical Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme implements multiple hierarchical data encryption of a single access policy, which reduces the computation and storage overhead. However, existing data hierarchical CP-ABE schemes have some problems, such as the leakage of personal privacy information through access policies or user attributes in plaintext form, and these schemes grant enough privileges to a single authorization center. If the authorization center is untrusted or attacked, keys can be used to illegally access data, which is the key escrow problem. To solve these problems, we propose an Attribute Hiding and Multiple Authorization Centers-based Data Hierarchical Encryption Scheme (AH-MAC-DHE). Firstly, we propose an Attribute Convergence Hiding Mechanism (ACHM). This mechanism solves the problem of personal privacy information leakage by hiding access policies and user attributes. Secondly, we design Privilege-Dispersed Multiple Authorization Centers (PD-MAC). PD-MAC solves the problem of key escrow by dispersing the privileges of the single authorization center to the user authorization center and attribute authorization center. Finally, we prove that AH-MAC-DHE is secure under the decisional q-parallel Bilinear Diffie-Hellman Exponent (BDHE) assumption, which also satisfies anti-collusion and privacy security. The experimental results indicate that compared with existing schemes, AH-MAC-DHE performs well.

Analysis of the Role of Compliance Plan in AI Criminal Risk Prevention-Take AI Criminal Risk in Network Communication as Example

To address the criminal risks associated with artificial intelligence (AI) in network communications, such as privacy invasion, information leakage and abuse, prejudice and discrimination, and intelligent crime, it's crucial to implement robust compliance plans. These plans should ensure legal compliance by adhering to relevant laws and regulations, thus safeguarding against unlawful use of AI. Strengthening data privacy and protection is vital to prevent unauthorized access and misuse of sensitive information. Ensuring fairness and eliminating discrimination are essential to maintaining ethical AI practices and preventing biases in AI decision-making processes. Improving system transparency and interpretability is also critical; it involves making AI systems more understandable and accountable for their actions and decisions. Additionally, reinforcing security measures is necessary to defend against cyber threats and vulnerabilities, thereby reducing the probability of AI- enabled criminal activities. These comprehensive strategies are pivotal in mitigating the criminal risks of AI in network communication and promoting the responsible and ethical development of AI technology.

Privacy-Preserving Cross-Area Traffic Forecasting in ITS: A Transferable Spatial-Temporal Graph Neural Network Approach

Traffic forecasting is essential in improving and maintaining safety and orderliness in intelligent transportation systems (ITS). As a deep learning approach, graph neural networks (GNN) based spatial-temporal association mining methods are promising in traffic forecasting. However, current GNN-based methods usually require a high number of training data, and when the sample volume is small, the performance of the model drops dramatically. The existing transfer methods can solve this problem by leveraging knowledge from other data-rich areas, but the domain adaption method with access to source data still faces the non-neglectable problem of private information leakage in the source area. A solution that can solve cross-area transfer without access to source data is still missing. In this paper, to fill the gap, we propose a Transferable Federated Inductive Spatial-Temporal Graph Neural Network (T-ISTGNN) framework to transfer spatial-temporal dependency information in cross-area data to accomplish traffic state forecasting. First, we introduce a multi-source model aggregation scheme based on federated learning to retain the traffic information of the source areas. Second, we propose a transfer method between source and target areas based on hypothesis transfer learning to achieve domain adaption under source domain data protection. Third, we propose a GNN-based method called Inductive Spatial-Temporal Graph Neural Network (ISTGNN) for traffic forecasting. Experiments on real-world datasets demonstrate that T-ISTGNN is capable of cross-area traffic state forecasting under the restriction of preserving the privacy of source areas.

Flexible and secure access control for EHR sharing based on blockchain

With the rapid development of the healthcare industry, many Electronic Health Records (EHRs) have emerged in different healthcare centers. However, lots of personal medical data are stored directly in plaintext at a single healthcare center, which makes it suffer from the single point of failure and brings many security and privacy issues such as privacy information leakage or tampering. The combination of blockchain and attributed-based cryptography can effectively solve the above problems. Therefore, in order to enable flexible fine-grained access control and efficient data retrieval while achieving privacy protection, we employ the ciphertext-policy attributed-based encryption (CP-ABE) and ciphertext-policy attribute-based searchable encryption (CP-ABSE) to propose a hidden policy attribute-based access control scheme. In addition, we combine consortium blockchain and smart contract to provide secure and reliable search and outsourcing decryption. Finally, through the security analysis and experimental results, we demonstrate that our scheme is secure and efficient.

How internet use affects personal privacy risk perception: empirical evidence from China

PurposeThis study aims to empirically examine the relationship between Internet use and personal privacy risk perceptions, the mediating effect of trust and the moderating effect of satisfaction on that relationship, which is exactly conducive to the practice of personal information protection.Design/methodology/approachA moderated mediation model will be employed to test the hypothesized relationships using the 2017 Chinese Society Survey data.FindingsThe authors find that Internet use positively relates to citizens' risk perceptions toward privacy security, and trust partially mediates the relationship between Internet use and privacy risk perception. In addition, the analysis of moderating effects showed that satisfaction with social life significantly enhances the negative impact on individuals' privacy risk perceptions of interpersonal trust. The positively moderating effect of satisfaction with local governments' work mainly reveals the relationship between interpersonal trust (or institutional trust) and citizens' privacy risk perception. Moreover, satisfaction with Internet platforms positively moderates the relationship between consumer trust and privacy risk perception.Originality/valueThis article contributes to the social risk amplification framework by applying it to the personal privacy information protection field, which was rarely discussed before. It also enriches privacy research by identifying the internal mechanism of how Internet use influences citizens' risk perceptions towards privacy information leakage.

동형암호를 활용한 앙상블학습방법의 추론

According to the recent amendment of data-related bills, active efforts are being made to conduct big data analysis for hyper-personalization services. Hyper-personalization services involve several data integration, including external data. However, it also inherently possesses limitations in usage due to concerns about private information leaks, which can lead to damages to individuals and businesses and unnecessary societal losses. One approach to mitigate such security risks is to use homomorphic encryption, which protects private information. In particular, homomorphic encryption fundamentally prevents private information leakage during data integration and analysis processes and retains original data. In this study, we develop an algorithm for performing inference using an ensemble learning method with homomorphic encryption and apply it to real financial complaint data. We demonstrate the equivalence between scores obtained from random forest and XGBoost in plaintext and those derived from the homomorphic ciphertext. Empirical results show that the algorithm achieves inference computation times in approximately 0.4 seconds, confirming the potential utility of the tree-based ensemble learning method.

Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond

Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their data stored locally, sharing only updates of the statistical model’s parameters obtained by minimizing a cost function over their local inputs. FL was proposed as a stepping-stone towards privacy-preserving machine learning, but it has been shown to expose clients to issues such as leakage of private information, lack of personalization of the model, and the possibility of having a trained model that is fairer to some groups of clients than to others. In this paper, the focus is on addressing the triadic interaction among personalization, privacy guarantees, and fairness attained by trained models within the FL framework. Differential privacy and its variants have been studied and applied as cutting-edge standards for providing formal privacy guarantees. However, clients in FL often hold very diverse datasets representing heterogeneous communities, making it important to protect their sensitive and personal information while still ensuring that the trained model upholds the aspect of fairness for the users. To attain this objective, a method is put forth that introduces group privacy assurances through the utilization of d-privacy (aka metric privacy). d-privacy represents a localized form of differential privacy that relies on a metric-oriented obfuscation approach to maintain the original data’s topological distribution. This method, besides enabling personalized model training in a federated approach and providing formal privacy guarantees, possesses significantly better group fairness measured under a variety of standard metrics than a global model trained within a classical FL template. Theoretical justifications for the applicability are provided, as well as experimental validation on real-world datasets to illustrate the working of the proposed method.

Quasi‐identifier recognition with echo chamber optimization‐based anonymization for privacy preservation of cloud storage

SummaryThe publication of data raises several security concerns, implying that when a reputable company offers data to a third party, personal information is not needed to be revealed. The fundamental issue that led to identity leakage is the connection of quasi‐identifiers (QIDs); however, the majority of researchers overlook the identification of accurate QIDs. This study developed a privacy preservation and clustering‐based quasi‐identification model based on the Echo Chamber optimization with a Z‐mixture parameter for privacy‐preserving combined data publishing to maintain the privacy of the data. The Echo chamber optimization is used to conduct a clustering‐based quasi‐identification to find the significant attributes especially, in determining the dimension of the solutions as well as the precise QIDs using the re‐identification risk rate as the fitness function. The loss of information is greatly reduced in terms of the metrics such as normalized certainty penalty metric, average equivalent class size metric, and discernibility metric. The developed optimized clustering‐based algorithm with the privacy preservation model extensively minimizes the leakage of private information and the utilization of data is well‐maintained compared with other existing algorithms.

Optimal Trading Mechanism Based on Differential Privacy Protection and Stackelberg Game in Big Data Market

Big data has become a fundamental resource and a commodity in economic activities, thus, it is necessary to build a market model capable of supporting efficient data trading. However, two major challenges remain. Firstly, researches have considered constructing data trading mechanisms, while few of them are based on the method of measuring data value in multiple dimensions. Secondly, a data market involved an intermediary trading platform (i.e., a third party) which is honest but curious, results may be obtained due the the leakage of private information. In this paper, we design TM-OUE, a data trading mechanism based on Optimized Unary Encoding that enables reasonable trading mechanism and protects the privacy of data trading. First of all, we combine qualitative and quantitative methods to measure the value of data in multiple dimensions and formulate data trading model between the data provider and data users. Then, we utilize an Optimized Unary Encoding (OUE) protocol to protect the privacy of the data trading mechanism. Based on the above steps, we develop a two-stage single leader multi-follower Stackelberg game to jointly maximize profits of the data provider and data users. Experimental results demonstrate that TM-OUE can offer appropriate price for data and maximize benefits both for data providers and data users, which guarantees fair data trades while protecting privacy.

MISPSO-Attack: An efficient adversarial watermarking attack based on multiple initial solution particle swarm optimization

The vulnerability of deep learning models to adversarial attacks is a growing concern, as the emergence of adversarial samples exposes almost all models to the risk of such attacks. This paper proposes a new method for adversarial attacks through watermarking. Our goal is to leverage the properties of adversarial samples to prevent people’s images from being maliciously collected and compared, thereby avoiding the leakage of private information. Our method, which improves on the multi-swarm particle swarm optimization (MPSO) algorithm, outperforms existing similar methods on two popular computer vision datasets. We conducted attack experiments on the widely used Imagenet dataset and achieve the highest attack success rate of 89.50%. The experimental results demonstrate the superiority of our method over existing similar methods. We simulate the attacks on the online social environment using two face photographs datasets and face recognition models. Our method reaches the best deception performance compared to similar methods, with the highest success rate of 97.03%, demonstrating our approach’s ability to protect individuals’ privacy. Furthermore, we investigate the natural causes of adversarial samples and demonstrate their inevitability, providing valuable insights for developing more robust deep models. The source code of the proposed method is available online at: https://github.com/grandwang/main_attack.

Wasserstein Distance-Based Deep Leakage from Gradients.

Federated learning protects the privacy information in the data set by sharing the average gradient. However, "Deep Leakage from Gradient" (DLG) algorithm as a gradient-based feature reconstruction attack can recover privacy training data using gradients shared in federated learning, resulting in private information leakage. However, the algorithm has the disadvantages of slow model convergence and poor inverse generated images accuracy. To address these issues, a Wasserstein distance-based DLG method is proposed, named WDLG. The WDLG method uses Wasserstein distance as the training loss function achieved to improve the inverse image quality and the model convergence. The hard-to-calculate Wasserstein distance is converted to be calculated iteratively using the Lipschit condition and Kantorovich-Rubinstein duality. Theoretical analysis proves the differentiability and continuity of Wasserstein distance. Finally, experiment results show that the WDLG algorithm is superior to DLG in training speed and inversion image quality. At the same time, we prove through the experiments that differential privacy can be used for disturbance protection, which provides some ideas for the development of a deep learning framework to protect privacy.

BSAF: A blockchain-based secure access framework with privacy protection for cloud-device service collaborations

In an open Internet-of-Things (IoT) environment, cloud-device collaborative development brings more functional services to users. However, privacy disclosure risks concerning service provision and users’ accessing behavior also increase. Traditional privacy protection approaches involve a centralized system with a third-party administrative server, which has the risk of single-point failures and overlooks the issue of privacy information leakage concerning users’ behaviors. Blockchain is a decentralized and traceable technology that provides a reliable solution for secure access by users. This paper proposes a novel blockchain-based secure access framework (BSAF) for cloud-device service collaborations with privacy protection. Specifically, a key matrix encryption mechanism is used to protect the privacy of users’ behaviors, and a fully homomorphic encryption mechanism is designed to protect the privacy of service content. Two smart contracts are designed: a request verification smart contract to verify the access rights of users, and a behavior punishment smart contract to audit users’ access behaviors. Comprehensive experiments on the Ethereum blockchain network show that the proposed BSAF framework outperforms existing schemes in latency reduction and cost saving, and is more suitable for low-profile IoT devices.

Trusted Framework for Online Banking in Public Cloud Using Multi-Factor Authentication Using Block-Chain Framework

There are always potential hazards from hackers, adware, malware, and viruses. Many big international corporations have experienced hacking and security breaches in the previous few years. In certain instances, this has resulted in the leakage of private and confidential information, such as bank account information, addresses, and transactions, but there are security measures in place that can block these things before they get too close to the company's private data. This is crucial for both secrecy and to avoid paying the steep fines levied on businesses that fail to adequately safeguard customer information. The project is a verification mechanism that only allows users with the proper input credentials to access the system. The project involves modules or user certificates. Security credentials come in a variety of forms. Thus, we incorporate a unique blockchain for banking security and user authentication in this proposed methodology. This degree of authentication reduces the possibility of hacking and the loss of sensitive data. Also, in the current environment, safer bank transactions are necessary due to advancements in security technologies. They have also made advances into industries, healthcare, telecommunications, and home automation, among others. A distributed ledger is basically what a blockchain is. It can keep information about who owns a certain piece of property or, for example, a bond. A permanent record of ownership can be maintained using technology, which also makes it possible for parties with a low level of trust to exchange the asset. Key Words: Custom Blockchain, Distributed Ledger, Authentications, Confidentiality, Transactions, Security System, Credentials, Banking Security, etc.

TSDroid: A Novel Android Malware Detection Framework Based on Temporal & Spatial Metrics in IoMT

In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the “sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid, and DroidEvolver) and obtain good promotion effects by our framework.