Leakage Of Private Information Research Articles

Scaling & fuzzing: Personal image privacy from automated attacks in mobile cloud computing

The mobile cloud computing (MCC) paradigm provides a range of useful services to smart phone users and enhances the user experience significantly. But, as MCC requires the data to be offloaded to an external server, there are serious concerns regarding the privacy of the users’ personal data such as images. For instance, a cloud server could perform image segmentation on user images to extract interesting artifacts such as restaurants, user’s clothing preferences, tourist locations, participation in social events and so on, which characterize the user’s personal life. The leakage of such private information could lead to milder consequences like targeted advertising or more serious consequences like identity theft. In this work, to protect the privacy of user images, we describe a privacy-preserving image filtering for mobile cloud computing that protects against automated inference attacks based on techniques like image segmentation. The key intuition of our approach is to leverage the inherent properties of the discrete Fourier transform (DFT), which transforms each image pixel into a complex value real and imaginary parts which can be processed independently. By dividing the image in this manner, we are able to process distinct parts of the image on different non-colluding servers and aggregate the results at the client. Furthermore, to prevent information leakage at individual servers, we obfuscate the data sent to any given server using an efficient reversible transformation. We prove our approach to be secure under the semi-honest model and non-colluding servers where at least one server does not collude with the rest of the servers. In comparison to the existing paradigm of outsourced privacy preserving computation, i.e., processing encrypted data using homomorphic encryption, our approach employs easy-to-implement obfuscation techniques without any key management overhead at the client. Using experimental evaluation as well as information theoretic leakage evaluation, we show that our approach is efficient and suitable for users of mobile devices.

A new construction of leakage-resilient CCA secure IBE scheme

Leakage of private information has become a threat to the security of cryptography systems. It has become a common security requirement that a cryptography scheme should withstand various leakage attacks. The non-interactive zero-knowledge (NIZK) argument system, one-time lossy filter (OT-LF) and one-time signature were widely used to create the generic constructions of leakage-resilient identity-based encryption (IBE) scheme with chosen-ciphertext attack (CCA) security. However, the computational efficiency of the corresponding generic construction is low because the underlying cryptographic tool is low. Thus, to solve the above problem, a new cryptographic primitive, called identity-based hash proof system with two encapsulated-key (T-IB-HPS), is proposed. The new generic constructions of leakage resilient IBE scheme with CCA security is created from the T-IB-HPS and message authentication code (MAC), and the security of the above proposed scheme is proved from the security of the underlying cryptographic tool. To further show the practicability, an instantiation of T-IB-HPS is constructed, and the formal security proof of the above instantiation is shown based on the decisional bilinear Diffie-Hellman (DBDH) assumption. Compared with the previous generic constructions of leakage resilient IBE scheme with CCA security, since the underlying cryptographic tools with low computational efficiency are not used, our generic construction has high computational efficiency.

Medical Sports Data Privacy Protection Method Based on Legal Risk Control.

With the continuous development of computer and network technology, the amount of information storage in medical information system is more and more large, which is prone to the problem of privacy information leakage, resulting in irreparable harm. In order to solve the problem of privacy leakage in the medical environment, a new privacy rating method is proposed according to the actual situation of the medical environment. The big data technology is used to effectively mine, analyze, integrate, and reuse medical data, and a new improved model is proposed. At the same time, the medical information system applying the improved model is designed according to the complex actual needs. The purpose of this paper is to correctly understand the positive role of medical sports big data (BD) research in the medical field and standardize the behavior of medical staff. On the one hand, it can improve the safety awareness of patients and enhance the standardization of medical treatment environment. This paper will analyze the meaning and research status of medical data from the perspective of legal risk control, focus on the status quo and existing problems of medical sports data privacy protection, and put forward positive countermeasures and some practical solutions. The results show that the medical sports information data has certain regularity and particularity, ease to spread, and mining. Hospitals and medical staff should make the areas and items restricted by law clear, standardize their own behaviors, constantly sum up experience, and actively improve and modify relevant measures.

Optimal Privacy-Aware Estimation

This paper studies the design of an optimal privacy-aware estimator of a public random variable based on noisy measurements which contain private information. The public variable carries also non-private information, but, its estimate will be correlated with the private information due to the estimation process. The objective is to design an optimal estimator of the public random variable such that the leakage of private information, via the estimation process, is kept below a certain level. The privacy metric is defined as the discrete conditional entropy of the private variable given the output of the estimator. We show that the optimal privacy-aware estimator is the solution of a (possibly infinite-dimensional) convex optimization problem when the estimator has access to either the measurement or the measurement together with the private information. We study the optimal perfect-privacy estimation problem that ensures the estimate of the public variable is independent of the private information. A necessary and sufficient condition is derived guaranteeing that an estimator satisfies the perfect-privacy requirement. It is shown that the optimal perfect-privacy

Analyzing and Evaluating Smart Cities for IoT Based on Use Cases Using the Analytic Network Process

With the passage of time, the world population is growing. Proper utilization of resources and other devices is tremendously playing an important role to easily examine, manage, and control the resources of the Internet of Things (IoT) in the smart city. Research in the field of IoT has revolutionized the services mostly in smart cities. In the smart city, the applications of IoT are utilized without human involvement. Diverse IoT devices are connected with each other and communicate for different tasks. With the existence of a huge number of IoT devices in the forthcoming years, the chances of privacy breach and information leakage are increasing. Billions of devices connected on IoT producing huge volume of data bound to cloud for processing, management, and storage. Sending of whole data to the cloud might create risk of security and privacy. Various needs of the smart city should be considered for both urgent and effective solutions to support requirements of the growing population. On the other side of rising technology, the IoT evolution has massively produced diverse research directions for the smart city. Keeping in view the use cases of the smart city, the proposed study presents the analytic network process (ANP) for evaluating smart cities. The approach of ANP works well in the situation of complexity, and vagueness exists among the available alternatives. The experimental results of the planned approach show that the approach is effective for evaluating the smart cities for IoT based on the use cases.

Blockchain‐based patient centric health care communication system

SummaryThe rapid increase in health care data breaches with the existing centralized systems emphasizes a decentralized health care system while ensuring reliability, privacy, security, and trust. Further, to ensure trust in the medical community, scientist, and pharmaceutical, it is essential to improve the quality of health care data management. In this direction, we proposed a blockchain‐based decentralized privacy‐preserving EMR management (DPEM), which can ensure accountability and integrity. We propose a four‐layered framework for DPEM consisting of a data preparation layer, access control and security layer, data sharing layer, and data storage layer with the objectives: (i) To provide privacy‐preserving in DPEM, we propose a new elliptic curve‐based content extraction signature (EC‐CES) through which patients can exclude EMR's sensitive information to eradicate leakage of privacy information in the data sharing process. (ii) To provide secure data sharing, blockchain smart contracts are used to define the predefined access permissions of the patients. (iii) To provide secure storage, we use a cloud facility to store actual EMRs, and consortium blockchain is used to store respective indexes of EMRs so that the data leakages of EMRs could be optimized and simultaneously, indexes in consortium blockchain will take care the integrity of EMRs. (iv) To provide access control in data sharing, we adopted ciphertext‐policy attribute‐based encryption (CP‐ABE) access control policy to empower the owners of data to secure the cloud storage and give access to authorized users through the encrypted link to the cloud storage with access control policies blinded. Finally, the security analysis demonstrates that DPEM is an optimized way of achieving EMRs secure data sharing.

Multi-keyword Cipher-text Retrieval Method for Smart Grid Edge Computing*

With the continuous deepening of China's power grid intelligent upgrading and the rise of edge computing technology, power grid terminals can carry out data storage and intelligent calculation at the edge of the power grid. However, the generated data by users may cause the leakage of user privacy information. The power grid edge system can retrieve the user's electricity data on demand, so as to improve the level of privacy protection and reduce the demand of transmission and computing resources through the localized storage of data. Therefore, how to carry out privacy protection and on-demand retrieval is a key problem to realize the intelligent power grid edge. In this paper, a multi-keyword cipher-text retrieval scheme (MCR- SGEC) for smart grid edge computing is proposed, which can achieve accurate matching of index and trap door, and has low spatial complexity. At the same time, on this basis, a multi-keyword fuzzy cipher-text retrieval method for smart grid edge computing (MFSE-SGEC) is proposed, which allows users to have spelling errors, which makes the user experience better.

Continuous leakage-resilient certificate-based signcryption scheme and application in cloud computing

Leakage of private information, e.g. the secret keys, has become a serious threat to the security of computing systems. It has become a common requirement that real-world security applications should withstand various leakage attacks, such as side-channel attacks, cold-boot attacks, etc. For example, the above leakage attacks are very common in cloud computing nowadays. Hence, we need a novel method to protect the security of data storage and authorization even if a certain amount of leakage information with respect to the secret state can be obtained by any adversary. In order to achieve the above goal, in this paper, we introduce a continuous leakage-resilient certificate-based signcryption (CBS) scheme, and we prove that our proposed scheme achieves the chosen-ciphertext attacks (CCA) security based on the discrete logarithm assumption and the decisional Diffie-Hellman assumption. Our proposed scheme not only has the ability to resist the continuous leakage attacks, but also enjoys very low computational overheads. Moreover, two concrete continuous leakage-resilient data storage and authorization protocols are generated from the above continuous leakage-resilient CBS scheme: one has a single key generation center and the other generates the keys in a distributed form. Therefore, our protocols with continuous leakage resilience are particularly suitable for data storage and authorization in cloud computing system.

Distributed optimal resource allocation over strongly connected digraphs: A surplus-based approach

In this brief, a surplus-based approach is proposed to solve the distributed optimal resource allocation problem over directed graphs. Two features distinguish our method from the existing distributed resource allocation schemes. The first one is that the proposed method can be implemented on general strongly connected graph and overcomes the asymmetry caused by the digraphs. The second one is that the proposed method enjoys the privacy-preserving property and avoids the privacy information leakage in the process of information exchange. The distributed algorithm is designed and analyzed by virtue of primal–dual methods, projected gradient, and eigenvalue perturbation theory. Furthermore, distributed economic dispatch in power system, as a simulation example, is used to illustrate the effectiveness of the presented distributed algorithm.

A Game-Theoretic Approach for Enhancing Data Privacy in SDN-Based Smart Grids

Smart grids rely on communication networks to connect the physical devices and the control and computation technologies. The transmission of sensitive data over the network induces the possibility of leakage of private and sensitive information about various entities and components in the grid. To address this issue, this article proposes a privacy-preserving framework to enhance the privacy of smart grids integrated with software-defined networks. The proposed framework uses two privacy metrics (mutual information and differential privacy) and formulates a privacy-preserving distributed optimization algorithm with the objective of minimizing the network cost. We view the distributed optimization algorithm as an n-player, noncooperative game and provide distributed techniques to solve the optimization problem efficiently. We prove that our algorithm converges to the Nash equilibrium of the game while preserving the data privacy. We validate the performance of our approach using three IEEE bus systems and realistic Internet service provider network topology.

Association Analysis of Private Information in Distributed Social Networks Based on Big Data

As people’s awareness of the issue of privacy leakage continues to increase, and the demand for privacy protection continues to increase, there is an urgent need for some effective methods or means to achieve the purpose of protecting privacy. So far, there have been many achievements in the research of location‐based privacy services, and it can effectively protect the location privacy of users. However, there are few research results that require privacy protection, and the privacy protection system needs to be improved. Aiming at the shortcomings of traditional differential privacy protection, this paper designs a differential privacy protection mechanism based on interactive social networks. Under this mechanism, we have proved that it meets the protection conditions of differential privacy and prevents the leakage of private information with the greatest possibility. In this paper, we establish a network evolution game model, in which users only play games with connected users. Then, based on the game model, a dynamic equation is derived to express the trend of the proportion of users adopting privacy protection settings in the network over time, and the impact of the benefit‐cost ratio on the evolutionarily stable state is analyzed. A real data set is used to verify the feasibility of the model. Experimental results show that the model can effectively describe the dynamic evolution of social network users’ privacy protection behaviors. This model can help social platforms design effective security services and incentive mechanisms, encourage users to adopt privacy protection settings, and promote the deployment of privacy protection mechanisms in the network.

A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks

With the advent of intelligent handheld devices, location sharing becomes one of the most popular services in mobile online social networks (mOSNs). In location-sharing services, users can enjoy a better social experience by updating their real-time location information. However, the leakage of private information may hinder the further development of location-sharing services. Although many solutions have been proposed to protect users’ privacy, the privacy-utility trade-offs must be considered. Therefore, we propose a new scheme called search efficient privacy-preserving location-sharing (SELS) system. In our scheme, we create a new approach named associated grids to improve the efficiency of location-sharing systems while maintaining users’ privacy. In addition, by setting the user-defined access control policy proposed in our scheme, users’ flexible privacy-preserving requirements can be satisfied. Detailed complexity and security analysis show that the proposed scheme is a practical and efficient privacy-preserving solution. Extensive simulations are performed to validate the effectiveness and performance of our scheme.

Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective

Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this paper, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensing framework, and bridge the gap between theoretical accuracy of NILM inference and differential privacy's parameters. We then derive the valid theoretical bounds to offer insights on how the differential privacy parameters affect the NILM performance. Moreover, we generalize our conclusions by proposing the hierarchical framework to solve the multi-shot NILM problem. Numerical experiments verify our analytical results and offer better physical insights of differential privacy in various practical scenarios. This also demonstrates the significance of our work for the general privacy preserving mechanism design.

WITHDRAWN: Privacy –Preserved in cloud based data sharing for energy management system using big data analytics

Abstract The fast increment in the volume of information created from associated gadgets is cloud network. The worldview opens up additional opportunities for improving the nature of administration for the rising applications through information sharing. In any case, security and protection concerns (e.g, information leakage) are significant deterrents for information suppliers to share their information in remote systems. The leakage of private information can prompt significant issues that lead to related misfortune for the suppliers. We first plan a data enabled secure information sharing engineering for dispersed various gatherings. At that point, we define the information sharing issue by consolidating security protected combined learning. The protection of information is all around kept up by sharing the information model as opposed to uncovering the real information. Numerical outcomes got from genuine world datasets show that the proposed information sharing plan accomplishes great exactness, high productivity, and upgraded security. We focus on seeking a solution that allows secure and effective access to the cloud data. In addition to ensuring strong data sharing security, the policy succeeds in preserving the privacy of cloud users. Security analysis indicates that the proposed policy is secured and enforces fine-grained access control and full collusion resistance simultaneously.

Data Sanitization to Reduce Private Information Leakage from Functional Genomics

The generation of functional genomics datasets is surging, because they provide insight into gene regulation and organismal phenotypes (e.g., genes upregulated in cancer). The intent behind functional genomics experiments is not necessarily to study genetic variants, yet they pose privacy concerns due to their use of next-generation sequencing. Moreover, there is a great incentive to broadly share raw reads for better statistical power and general research reproducibility. Thus, we need new modes of sharing beyond traditional controlled-access models. Here, we develop a data-sanitization procedure allowing raw functional genomics reads to be shared while minimizing privacy leakage, enabling principled privacy-utility trade-offs. Our protocol works with traditional Illumina-based assays and newer technologies such as 10x single-cell RNA sequencing. It involves quantifying the privacy leakage in reads by statistically linking study participants to known individuals. We carried out these linkages using data from highly accurate reference genomes and more realistic environmental samples.

Privacy-Enhanced Data Fusion for COVID-19 Applications in Intelligent Internet of Medical Things.

With the worldwide large-scale outbreak of COVID-19, the Internet of Medical Things (IoMT), as a new type of Internet of Things (IoT)-based intelligent medical system, is being used for COVID-19 prevention and detection. However, since the widespread use of IoMT will generate a large amount of sensitive information related to patients, it is becoming more and more important yet challenging to ensure data security and privacy of COVID-19 applications in IoMT. The leakage of private information during IoMT data fusion process will cause serious problems and affect people’s willingness to contribute data in IoMT. To address these challenges, this article proposes a new privacy-enhanced data fusion strategy (PDFS). The proposed PDFS consists of four important components, i.e., sensitive task classification, task completion assessment, incentive mechanism-based task contract design, and homomorphic encryption-based data fusion. The extensive simulation experiments demonstrate that PDFS can achieve high task classification accuracy, task completion rate, task data reliability and task participation rate, and low average error rate, while improving the privacy protection for data fusion under COVID-19 application environments based on IoMT.

Channel Coding Scheme for Relay Edge Computing Wireless Networks via Homomorphic Encryption and NOMA

Edge computing extends the service resources of cloud computing to the edge of the network, providing less latency and higher bandwidth utilization. The relay edge computing wireless network (RECWN) with intelligent edge device can effectively solve the problem of wireless signal transmission distance and attenuation. However, the relay node’s forwarding brings greater risks to the leakage of private information. In this article, we propose a homomorphic encryption and non-orthogonal multiple access (NOMA) based channel coding scheme to address the problem of stealing private information from users by incompletely trusted relay node or potential malicious nodes. Specifically, the homomorphic encryption algorithm is adopted to protect the privacy of users’ private information. Furthermore, the code domain NOMA and LDPC algorithm are introduced to compensate for the problem of drastic drop in communication performance caused by poor sub-channel conditions, thereby improving the efficiency of data transmission, and guaranteeing the users’ QoS requirements and fairness. The simulation results demonstrate that the proposed channel coding scheme has better performance than the existing approaches in terms of privacy preservation and data transmission efficiency.

A privacy-preserving mechanism based on local differential privacy in edge computing

With the development of Internet of Things (IoT), the delay caused by network transmission has led to low data processing efficiency. At the same time, the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain, but edge computing could solve this problem. The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity. However, user data in edge computing is usually stored and processed in some honest-but-curious authorized entities, which leads to the leakage of users' privacy information. In order to solve these problems, this paper proposes a location data collection method that satisfies the local differential privacy to protect users' privacy. In this paper, a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located. A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid. In addition, the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments. Compared with the existing privacy-preserving methods, the proposed privacy-preserving mechanism can not only better meet users' privacy needs, but also have higher data availability.

Proprietary information and the cost of bank debt

PurposeThis study explores how the firm’s proprietary information has an impact on the bank loan contracts. It explains the propensity of using the competitive bid option (CBO) in the syndicate loans to solicit the best bid for innovative firms and how it changes based on industry competition and the degree of innovations. This research also examines how the interstate banking deregulation (Interstate Banking and Branching Efficiency Act) in 1994 affected the private loan contracts for innovative borrowers.Design/methodology/approachThe study uses various econometric analyses. First, it uses the propensity score matching analysis to see the impact of patents on pricing terms. Second, it uses the two-stage least square (2SLS) analysis by implementing the litigation and non-NYSE variables. Finally, it studies the impact of the policy change of the Interstate Banking and Branching Efficiency Act of 1994 on the bank loan contracts.FindingsFirms with more proprietary information pays more annual facility fees but less other fees. The patents are the primary determinants of the usage of CBO in the syndicate loans to solicit the best bid. While innovative firms can have better contract conditions by the CBO, firms with more proprietary information will less likely to use the CBO option to minimize the leakage of private information and the severe monitoring from the banks. Finally, more proprietary information lowered the loan spread for firms dependent on the external capital after the interstate banking deregulation.Originality/valueThe findings of this research will help senior executives with responsibility for financing their innovative projects. In addition, these findings should prove helpful for the lawmakers to boost economies.

SPSC: A secure and privacy‐preserving autonomous platoon setup and communication scheme

SummaryThe new driving model of autonomous platoon has outlined the blueprint for developing the intelligent transportation of system since it can improve safe driving, alleviate traffic congestion and bring more comfortable experience. Vehicles in the platoon can communicate and share driving data with each other through vehicular network for cooperatively driving. Therefore, it is crucial to avoid some misbehavior vehicles which can eavesdrop or even tamper important data. In this paper, we propose a secure and privacy‐preserving autonomous platoon setup and communication scheme, called SPSC, to help vehicles establish secure communication and avoid private information leakage. Specially, considering the dynamic and temporary characteristics of the autonomous platoon, the autonomous platoon communication can be divided into two scenarios, that is, intra‐platoon communication and inter‐platoon communication. In the intra‐platoon communication, the SPSC can securely set up platoon and dynamically manage the platoon members with privacy preservation by using private set intersection and group key agreement techniques, respectively. In the inter‐platoon communication, the SPSC can also achieve secure and anonymous communication by adopting certificateless ring signcyption technique. In addition, vehicle‐out‐platoon can trace the disputed or misbehavior platoon member with the assistance of Road Transportation Authority. The security analysis shows that SPSC can provide authentication, privacy protection, confidentiality and traceability. Finally, the performance of SPSC is comprehensively evaluated by simulation to demonstrate its efficiency in terms of the computation cost.