Privacy Of Owners Research Articles

SIoT identity management with the ability to preserve owner privacy and built‐in resistance to Sybil attacks

SummaryThe Social Internet of Things (SIoT) allows for more efficient and intelligent communication between entities (humans and objects). Managing the digital identity of objects, protecting the owner's privacy, and resisting impersonation attacks are crucial challenges in SIoT. On the other hand, the classic centralized identity provider servers contain valuable information for hackers and malicious parties. Effective solutions can be presented using decentralized technologies such as blockchains. This paper proposes a decentralized identity management system for SIoT based on smart contract technology. This system drives object identity control; conversely, a zero‐knowledge‐based protocol and double‐spend prevention ideas are proposed for privacy preservation and Sybil attack resistance, respectively. The central idea is to implement self‐sovereign identity (SSI) so that even certifiers are not privy to object‐sensitive information. Additionally, it attempts to prevent the owner from creating Sybil objects using the web of trust method. The method's scalability and convergence can be proven by considering all acknowledgment nodes and different paths. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks.

Achieving federated logistic regression training towards model confidentiality with semi-honest TEE

In the distributed machine learning field, federated learning (FL) serves as a highly effective framework for dismantling data silos and integrating data from multiple sources. However, the flourishing of FL still encounters significant challenges, particularly in the realms of data security and information privacy. In recent years, plenty of privacy-preserving FL schemes have been proposed, yet few of them support global model confidentiality protection for the training server. In this paper, by combining Trusted Execution Environment (TEE) and homomorphic encryption, we propose a TEE-assisted federated logistic regression training scheme with model confidentiality protection, named TFLR. Specifically, we first formalize a cryptography-TEE hybrid security model under the multi-party cooperative computation scenario. Subsequently, within this hybrid security model, we design a series of secure computation protocols with semi-honest TEE to execute non-linear operations while maintaining the encryption form of global model parameters. Therefore, the global model of the training server can be well protected. In addition, TFLR incorporates a double masking technique, further fortifying the privacy of data owners' local updates. Detailed security analysis shows that TFLR is able to protect all sensitive information of data owners and the training server. Furthermore, we evaluate the performance of TFLR with real machine learning datasets, and the results substantiate that TFLR is indeed efficient.

Big Data Trade Survey Pricing, Trading, and Data Protection

In this era, Big Data is considered as the key to unlocking the growth of people's productivity in daily life. Internet usage, mobile applications and social networks, as well as the internet of things based on smart-grid and so on has an impact on the amount of data collected. The advancement of analytics data provided by machine learning and data mining technology supported by cloud computing, the resulting information will be more useful. Since the advent of Big Data, the dataset has become one type of "new money" in the digital world. Copyright protection mechanisms including digital encryption and watermarking need to be done to maintain data values. To maximize this, an effective container is needed to allow data owners and buyers to trade the data. The focus of the topic is on designing data trading platforms and schemes, supporting effective and efficient data trading, security, and maintaining the privacy of data owners.

Communication Privacy Management Theory in the Context of the COVID-19 Pandemic: Case Studies from the Philippines

Information management during the COVID-19 pandemic ranges from personal to institutional level of agencies. This was highly observable at the onset of the national health emergency in the Philippines when most Filipinos who had symptoms did not know what to do. They, as well as their relatives, were in a fearful situation, and so were the government and private agencies involved in health care services. When in need of medical attention, will they tell the truth about their travel history or their exposure to anyone who is infected? The researchers studied the turbulence that occurred when mutually held rules by a community were breached by its members through concealment and gatekeeping during national health emergencies. The study elucidates how the management of private information prevents boundary turbulence and maintains personal relationships, but results in uncontrolled social turbulence during the pandemic. Applicability of the system and principles of Communication Privacy Management Theory such as privacy ownership, privacy control, privacy turbulence, and information stakeholders were studied qualitatively through data generated from nine participants of varying points of view - a) patients and/or relatives who managed their private information; and b) patients, their relatives, and health workers such as doctors, nurses, and medical staff who were adversely affected by such management of private information. Keywords: COVID-19, communication, privacy, ownership, control.

Advancing Fingerprint Template Generation and Matching With Recast Minutiae Clustering and mRBFN

Rapid development of automation in the day-to-day life activity marks up the need of securing bio-metric template and the privacy of rightful owner. Minutiae-based matching is the most popular in the fingerprint recognition system, which greatly suffers from non-linear distortion like translation and rotation. To deal with linear distortion most of the technique proposed in the literature depends upon a reference or singular point. The paper proposes a binary template generation technique which applies an unsupervised clustering technique without fixing the number of clusters. Instead of position and orientation of the minutiae points the cardinality of the clusters are stored and converted into binary template. No spatial pattern information about the fingerprint is stored in the template to protect it from spoofing and information leakage. By the help of modified Radial Basis Function Network (mRBFN) with robust and efficient matching technique the generated templates are matched for authentication. We use MCYT dataset for training the mRBFN. The efficiency of the proposed scheme is evaluated on FVC 2000, FVC 2002 and FVC 2004 dataset.

Dual Attribute-Based Auditing Scheme for Fog Computing-Based Data Dynamic Storage With Distributed Collaborative Verification

Compared with cloud computing-based data storage, distributed data storage in fog computing is more vulnerable to malicious attacks. So, it is very necessary to provide a secure distributed auditing mechanism with protecting the identity privacy of data owners and controlling the identities of auditors under fog computing-based data storage. In this paper, we propose a dual attribute-based auditing scheme for fog computing-based data dynamic storage. Our auditing scheme can protect the identity privacy of data owners, and provide an attribute-based access control for corresponding audits with a distributed collaborative verification between related fog servers. In our scheme, a data owner can securely upload his divided and blinded file blocks with corresponding block authenticators (related with his attribute set) to related fog servers. To prevent malicious auditors from consuming system resources by abusing audit requests, the data owner can provide an attribute-based access control for corresponding audits, where the data owner specifies the attribute set of corresponding auditors who have the right to check the integrity of related data. Further, a distributed collaborative verification mechanism between related fog servers is constructed to reduce the disadvantages of centralized verification, where the Shamir’s secret-sharing method is used to decompose the picked blinding factor as the shared sub-secrets sent to each fog server respectively. Compared with cloud computing-based data storage, our collaborative verification mechanism can implement distributed auditing consent of stored data between multiple fog servers. Our auditing scheme can further audit out specific suspicious fog servers. Additionally, we provide a dynamic data operation mechanism to efficiently support the updating of users’ data under fog computing-based data storage. Furthermore, related theoretical analysis and experimental evaluation show our scheme is secure and efficient.

Geometric Model of 3D Cadastre for Strata Title using 2D and 3D Data Integration Approach

In order to establish the Land Administration System (LAS) in Strata Title case, the concept of 3D Cadastre is used widely. The semantic and geometric of 3D spatial unit (3D rights) in the building must be conducted first, but to provide such 3D spatial data is complicated, especially related to the owner privacy. Furthermore, the higher the Level of Detail (LOD) and Level of Information (LOI) of 3D model, the more complex the data acquisition and the higher accuracy needed will be. Therefore, the concept of 3D Cadastre could be adopted where the availability of 2D data still usable, such as the data of as-built-drawing of the building. However, how to do the integration of 2D and 3D spatial data and their accuracy analysis needs to be examined first, as the purpose of this study. The case study of this research is ‘Labtek IXC’, located in Ganesha Campus of Institut Teknologi Bandung. The 3D data was conducted using High-Definition Survey in available room and the 2D data was as-built-drawing in unavailable room (available and unavailable due the ownership privacy aspect). The High-Definition Survey produced the point cloud data. The point cloud data was processed using common steps such as filtering and cloud-to-cloud registration. The data modelling method was used the parametric modelling based on the registered point cloud and as-built-drawing of the building. The result of this study is the integration of 3D model. The geometric of the 3D model was compared with the reference size of the room from the distometer measurement. The geometric accuracy of the 3D model produced satisfactory result, that is 0.019 m.

A federated machine learning approach for order-level risk prediction in Supply Chain Financing

Supply Chain Financing (SCF) is increasingly utilised as an effective method for optimising cash flows in supply networks. With increased popularity several financial institutions have begun offering SCF to businesses. However various recent scandals have highlighted inefficiencies in the evaluation of risks involved. In this paper we argue this is due to a mismatch between the firm-level features used to evaluate risk and what SCF is given for, which is a particular order. However order-level risk evaluation is difficult as companies do not wish to share their datasets with funders. Furthermore, Small-to-Medium Enterprises (SMEs) themselves may not have enough data to conduct order-level risk evaluation. We propose a Federated Learning (FL) framework to overcome these issues, opening up the possibility for order-level risk evaluation. FL allows collective, order-level model training whilst preserving privacy of data owners. A case study in the aerospace industry indicates that FL can be applied to predict buyers’ late payment risk with minimal performance loss.

Exploring Privacy-Preserving Methods via Perturbation Data Mining Employing Diverse Noise Strategies

Knowledge discovery from data, commonly referred to as data mining. it involves the extraction of significant information, which may be previously unknown, concealed, or relevant, from extensive data sets or databases through the utilization of statistical methodologies. With the introduction of enhanced hardware technologies, there has been a proliferation in the storage and recording of personal data pertaining to individuals. Sophisticated organizations employ data mining algorithms to uncover hidden patterns or insights within data. Data mining techniques find application in diverse fields such as marketing, medical diagnosis, forecasting system, and national security. However, in scenarios where data privacy is paramount, mining certain types of data without violating the privacy of data owners presents a formidable challenge, sparking growing concerns among privacy advocates. To address these concerns, it is imperative to advance data mining procedures that are complex to individual privacy considerations. Perturbation of data plays a pivotal role in Privacy-Preserving Data Mining (PPDM). Additive data safeguard data privacy. In contrast, multiplicative data perturbation involves a series of transformations, including rotation, translation, and the addition of noise components to the perturbed data copy.

Locally Differentially Private Personal Data Markets Using Contextual Dynamic Pricing Mechanism

Data is becoming the world's most valuable asset and the ultimate renewable resource. This phenomenon has led to online personal data markets where data owners and collectors engage in the data sale and purchase. From the collector's standpoint, a key question is how to set a proper pricing rule that brings profitable tradings. One feasible solution is to set the price slightly above the owner's data cost. Nonetheless, data cost is generally unknown by the collector as being the owner's private information. To bridge this gap, we propose a novel learning algorithm, modified stochastic gradient descent (MSGD) that infers the owner's cost model from her interactions with the collector. To protect owners' data privacy during trading, we employ the framework of local differential privacy (LDP) that allows owners to perturb their genuine data and trading behaviors. The vital challenge is how the collector can derive the accurate cost model from noisy knowledge gathered from owners. For this, MSGD relies on auxiliary parameters to correct biased gradients caused by noise. We formally prove that the proposed MSGD algorithm produces a sublinear regret of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(T^{\frac{5}{6}}\sqrt{\log (T^{\frac{1}{3}})})$</tex-math></inline-formula> . The effectiveness of our design is further validated via a series of in-person experiments that involve 30 volunteers.

Blockchain-Based Deduplication and Integrity Auditing Over Encrypted Cloud Storage

Cloud computing promises great advantages in handling the exponential data growth. Secure deduplication can greatly improve cloud storage efficiency while protecting data confidentiality. In the meantime, when data are outsourced to the remote cloud, there is an imperative need to audit the integrity. Most existing works only consider the support for either secure deduplication or integrity auditing. Recently, there have been some research efforts aiming to integrate secure deduplication with integrity auditing. However, prior works are unsatisfactory in that they suffer from the leakage of ownership privacy and forgeability of auditing results for low-entropy data. In this paper, we propose a new scheme that delicately bridges secure deduplication and integrity auditing in encrypted cloud storage. In contrast with prior works, our scheme protects the ownership privacy and prevents the cloud service provider from forging the auditing results for low-entropy data. Furthermore, we propose a blockchain-based mechanism that helps to ensure key recoverability and reduce local storage cost of keys. Formal analysis is provided to justify the security guarantees. Experiment results demonstrate the modest performance overhead of our scheme.

Collaborative training of deep neural networks for the lithium-ion battery aging prediction with federated learning

Accurate and reliable prediction of the future capacity degradation of lithium-ion batteries is crucial for their application in electric vehicles. Recent publications have highlighted the effectiveness of deep learning, in particular, in generating precise forecasts regarding the aging patterns. However, large quantities of training data covering various aging behaviors are required to train such models effectively. Collecting such a large database centrally is not feasible due to privacy and data communication restrictions of data owners, such as testing facilities or fleet operators. Federated learning provides a solution to this open issue. A framework, which incorporates federated learning into the training of a data-based battery aging model, is presented in this paper. The benefit of federated learning is that even data owners with sensible information can participate in a collaborative model training, since the model training is only conducted locally and all the data remains local and does not have to be disclosed. Thus, more data owners are likely to participate in this collaborative training. This will improve the prediction performance due to the enlarged dataset that can be utilized for the model training. This work shows that the prediction accuracy of the model trained with federated learning is only slightly worse than the prediction results obtained by the ideal case in which all aging data is stored in a central database. A sensitivity analysis is presented to prove the robustness of federated learning even if the datasets between participating data owners are highly imbalanced or exhibit different aging behaviors. Within exemplary scenarios, it is shown that individual data holders can reduce their prediction errors from MAPEmean=7.07% to MAPEmean=0.91% by participating in the proposed federated learning-based framework.

Broadcast and Silence Period (BSP): A Pseudonym Change Strategy

Vehicles in Internet of Vehicles must constantly broadcast basic safety messages to keep other vehicles informed of their location, speed, and other parameters. Attackers can use the content of these messages to follow the path of the vehicle and invade vehicle owner privacy. Vehicle location privacy was ensured by broadcasting these messages using pseudonyms and periodically changing these pseudonyms. However, in mix-zone-based strategies, vehicles are frequently restricted from changing their pseudonyms in specific places. Furthermore, most previous change strategies only safeguarded against the syntactic linking attack and did not account for the semantic linking attack. Thus, in this study, we proposed a broadcast and silence period pseudonym change strategy to overcome these difficulties. Mix zones were obtained by analyzing the locations where vehicles frequently stop. The vehicle broadcasts or silences in a mix zone according to our broadcast and silence period strategy and changes to the pseudonym assigned by the road side unit during the silence phase until it leaves the zone. Our safety analysis and experimental results indicate that the broadcast and silence period prevent both linking attacks while reducing the impact of radio silence on vehicle safety by limiting the silent time, thereby providing better location privacy protection for vehicles compared with the existing schemes. In addition, the number of signatures required to be verified each time it receives messages and the overhead incurred in changing pseudonyms for vehicles is acceptable.

A Survey of Data Pricing for Data Marketplaces

A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data. A considerable number of studies on data pricing can be found in literature. This paper attempts to comprehensively review the state-of-the-art on existing data pricing studies to provide a general understanding of this emerging research area. Our key contribution lies in a new taxonomy of data pricing studies that unifies different attributes determining data prices. The basis of our framework categorises these studies by the kind of market structure, be it sell-side, buy-side, or two-sided. Then in a sell-side market, the studies are further divided by query type, which defines the way a data consumer accesses data, while in a buy-side market, the studies are divided according to privacy notion, which defines the way to quantify privacy of data owners. In a two-sided market, both privacy notion and query type are used as criteria. We systematically examine the studies falling into each category in our taxonomy. Lastly, we discuss gaps within the existing research and define future research directions.

Role Based Secure Data Access Control for Cost Optimized Cloud Storage Using Data Fragmentation While Maintaining Data Confidentiality

The paper proposes a role-based secure data access control framework for cost-optimized cloud storage, addressing the challenge of maintaining data security, privacy, integrity, and availability at lower cost. The proposed framework incorporates a secure authenticity scheme to protect data during storage or transfer over the cloud. The framework leverages storage cost optimization by compressing high-resolution images and fragmenting them into multiple encrypted chunks using the owner's private key. The proposed approach offers two layers of security, ensuring that only authorized users can decrypt and reconstruct data into its original format. The implementation results depicts that the proposed scheme outperforms existing systems in various aspects, making it a reliable solution for cloud service providers to enhance data security while reducing storage costs.

IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing

Achieving efficient and secure sharing of data in the Internet of Vehicles (IoV) is of great significance for the development of smart transportation. Although blockchain technology has great potential to promote data sharing and privacy protection in the context of IoV, the problem of securing data sharing should be payed more attentions. This paper proposes an IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing. Firstly, to improve protocol’s efficiency, a dual-chain structure empowered by alliance chain is introduced as the model architecture. Secondly, for the space problem characterized by data storage and security, we adopt distributed storage with the help of edge devices. Finally, to both ensure the efficiency of consensus protocol and protect the privacy of vehicles and owners simultaneously, we improve DPoS consensus algorithm to realize the efficient operation of the IoV data sharing model, which is closer to the actual needs of IoV. The comparison with other data sharing models highlights the advantages of this model, in terms of data storage and sharing security. It can be seen that the improved DPoS has high consensus efficiency and security in IoV.

Verifiable outsourcing EMRs scheme with attribute-based encryption in cloud-edge environments

With emerging of cloud and edge computing, it is popular to store Electronic Medical Records (EMRs) on a remote cloud. Furthermore, to share the EMRs stored in the cloud, attribute-based encryption (ABE) can provide a fine-grained access control strategy. However, in ABE, the finer the access control is, the bigger the number of required attributes is. Accordingly, it requires the higher computation costs. In this paper, to reduce the burden of patients, we first present a verifiable outsourced ABE scheme with the help of edge servers and fog nodes, in which edge servers are fully responsible for the encryption of ABE while fog nodes assist data users to perform the corresponding decryption. Furthermore, to protect the privacy of data owner, we introduced the proxy re-encryption algorithms, which still can achieve dual access control for EMRs. What's more, to detect the honesty of edge servers and the cloud, we adopt an aggregated digital signature to guarantee the correctness of the EMRs initially encrypted by data owner. The experimental results and security proofs show that our scheme has higher computational efficiency and achieves more fine-grained access control.

Dynamic identity‐based broadcast proxy re‐encryption for data sharing in autonomous vehicles

SummaryAutonomous vehicles (AVs), as the essential part of intelligent autonomous transportation system, improves vehicle communication efficiency and supports diverse kinds of social services. However, with the development of AVs, more data has been generated by the sensors about the road, traffic, street light, and weather conditions. This also inevitably bringing new security problems, such as secure data sharing. Although the identity‐based broadcast proxy re‐encryption (IBBPRE) can be used to realize one‐to‐many data sharing, most existing schemes require access to the data owner for the group shared key update phases since the data owner's private key is mandatory for the group shared key generation. Therefore, frequent interaction with the data owner increases the operational burden and risks leaking the data owner's private key. Thus, we design a dynamic identity‐based broadcast proxy re‐encryption (DIB‐BPRE) to solve the problem of re‐encryption update. In the proposed scheme, a vehicle cloud server directly revokes or adds a group of data receivers, designated by the data owner, from the re‐encryption key. Finally, the theoretical and experimental analyses demonstrate that the DIB‐BPRE scheme is efficient, and it is suitable for data sharing in AVs.

Privacy-Preserving Medical Data-Sharing System with Symmetric Encryption Based on Blockchain

Nowadays, data between hospitals are usually not interoperable, which brings great inconvenience to medical data sharing and patients’ medical treatment. In addition, patients do not want their medical data to be leaked during the sharing process. Researchers have employed blockchain to build data-sharing systems to address these issues. However, current systems do not restrict the power of participants, nor do they prevent visitors from sharing the obtained data to unauthorized parties. To address these issues, we propose a private data-sharing system with symmetric encryption for the medical industry that implements power restriction and access control, and prevents the leakage of private data. To be specific, firstly, symmetric encryption algorithm is utilized to encrypt medical data to protect the privacy of data owner. Secondly, our proposed system is built on a new blockchain framework, in which only visitors with permission can access the medical data. Thirdly, we employ chameleon signature to prevent visitors from sharing data with other parties without permission. Finally, we make the power of participants in the system revocable to prevent them from abusing their power. Our proposed system has been proven to be secure through security analysis and can protect the privacy of patients. In addition, the experimental results show that our system has excellent performance in terms of time overhead compared to other systems.

A Privacy Frequent Itemsets Mining Framework for Collaboration in IoT Using Federated Learning

Rapid advancement of industrial internet of things (IoT) technology has changed the supply chain network to an open system to meet the high demand for individualized products and provide better customer experiences. However the open-system supply chain has forced many small and midsize enterprises (SMEs) to adopt vertical integration by being divided into smaller companies with a distinctive business for each SME but a central alliance to produce a range of products and gain competencies. Therefore, existing models do not guarantee the protection of data privacy of individual SMEs. Moreover, especially for the IoT environment, collecting data in a secure way and revealing valuable knowledge in an IoT network is difficult. How to share data in a secure framework is of paramount importance in the internet of behavior field. In this article, a privacy-preserving data-mining framework is proposed for joint-venture industrial collaborative activities by combining federated learning and a “pre-large concept” of data-mining techniques. The novelty of the proposed approach is that, while mining high-utility itemsets (HUIs) from multiple datasets, it does not require direct data sharing. In the proposed method, the federated-learning framework can learn from aggregated learning parameters without scanning all data from different sets. The pre-large concept in this approach reduces the amount of scanning into different datasets. Thus, the approach makes it possible to train federated learning more quickly while protecting the privacy of individual data owners. The approach has been tested on real industrial datasets in a collaborative environment. Extensive experimental results show that the approach achieves high accuracy compared with conventional data-mining techniques while preserving the privacy of datasets.