scholarly journals A Privacy-Preserving Blockchain Supervision Framework in the Multiparty Setting

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Baodong Wen ◽  
Yujue Wang ◽  
Yong Ding ◽  
Haibin Zheng ◽  
Hai Liang ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Privacy Protection ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
User Privacy ◽  
Supervision System ◽  
Transaction Data ◽  
Massive Growth ◽  
Attribute Based Encryption ◽  
User Data

Data supervision is an effective method to ensure the legality of user data on blockchain. However, the massive growth of data makes it difficult to achieve data supervision in existing blockchain applications. Also, data supervision often leads to problems such as disclosure of transaction data and user privacy information. To address these issues, this paper proposes a privacy-preserving blockchain supervision system (BSS) in the multiparty setting, where a supervision chain is introduced to realize data supervision on blockchain. All sensitive information such as user information in the supervising data is encrypted by the attribute-based encryption (ABE) technology, so that both privacy protection and access control on user data can be achieved. Theoretical analysis and comparison show that the proposed BSS scheme is efficient, and experimental analysis indicates the practicality of our BSS scheme.

A Secure and Privacy-Preserving Approach to Protect User Data across Cloud based Online Social Networks

2021 ◽  
pp. 560-585
Author(s):  
Neelu khare ◽  
Kumaran U.
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Data Access ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Privacy Concerns ◽  
Attribute Based Encryption ◽  
User Data ◽  
Trapdoor Function

The tremendous growth of social networking systems enables the active participation of a wide variety of users. This has led to an increased probability of security and privacy concerns. In order to solve the issue, the article defines a secure and privacy-preserving approach to protect user data across Cloud-based online social networks. The proposed approach models social networks as a directed graph, such that a user can share sensitive information with other users only if there exists a directed edge from one user to another. The connectivity between data users data is efficiently shared using an attribute-based encryption (ABE) with different data access levels. The proposed ABE technique makes use of a trapdoor function to re-encrypt the data without the use of proxy re-encryption techniques. Experimental evaluation states that the proposed approach provides comparatively better results than the existing techniques.

A Secure and Privacy-Preserving Approach to Protect User Data across Cloud based Online Social Networks

2020 ◽  
Vol 12 (2) ◽  
pp. 1-24
Author(s):  
Neelu khare ◽  
Kumaran U.
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Data Access ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Privacy Concerns ◽  
Attribute Based Encryption ◽  
User Data ◽  
Trapdoor Function

The tremendous growth of social networking systems enables the active participation of a wide variety of users. This has led to an increased probability of security and privacy concerns. In order to solve the issue, the article defines a secure and privacy-preserving approach to protect user data across Cloud-based online social networks. The proposed approach models social networks as a directed graph, such that a user can share sensitive information with other users only if there exists a directed edge from one user to another. The connectivity between data users data is efficiently shared using an attribute-based encryption (ABE) with different data access levels. The proposed ABE technique makes use of a trapdoor function to re-encrypt the data without the use of proxy re-encryption techniques. Experimental evaluation states that the proposed approach provides comparatively better results than the existing techniques.

scholarly journals Privacy Preserving Spatio-Temporal Databases Based on k-Anonymity

2020 ◽  
Vol 3 (SI1) ◽  
pp. SI82-SI94
Author(s):  
Anh Tuan Truong
Keyword(s):  
Data Mining ◽  
Privacy Protection ◽  
Location Privacy ◽  
Location Based Services ◽  
Sensitive Information ◽  
User Privacy ◽  
Location Data ◽  
Related Information ◽  
Spatio Temporal ◽  
Location Privacy Protection

The development of location-based services and mobile devices has lead to an increase in the location data. Through the data mining process, some valuable information can be discovered from location data. In the other words, an attacker may also extract some private (sensitive) information of the user and this may make threats against the user privacy. Therefore, location privacy protection becomes an important requirement to the success in the development of location-based services. In this paper, we propose a grid-based approach as well as an algorithm to guarantee k-anonymity, a well-known privacy protection approach, in a location database. The proposed approach considers only the information that has significance for the data mining process while ignoring the un-related information. The experiment results show the effectiveness of the proposed approach in comparison with the literature ones.

scholarly journals FPETD: Fault-Tolerant and Privacy-Preserving Electricity Theft Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Siliang Dong ◽  
Zhixin Zeng ◽  
Yining Liu
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Fault Tolerant ◽  
Privacy Preserving ◽  
User Privacy ◽  
Accuracy Rate ◽  
Electricity Theft ◽  
User Data ◽  
Dimensionality Reduction Method ◽  
Better Than

Electricity theft occurs from time to time in the smart grid, which can cause great losses to the power supplier, so it is necessary to prevent the occurrence of electricity theft. Using machine learning as an electricity theft detection tool can quickly lock participants suspected of electricity theft; however, directly publishing user data to the detector for machine learning-based detection may expose user privacy. In this paper, we propose a real-time fault-tolerant and privacy-preserving electricity theft detection (FPETD) scheme that combines n -source anonymity and a convolutional neural network (CNN). In our scheme, we designed a fault-tolerant raw data collection protocol to collect electricity data and cut off the correspondence between users and their data, thereby ensuring the fault tolerance and data privacy during the electricity theft detection process. Experiments have proven that our dimensionality reduction method makes our model have an accuracy rate of 92.86% for detecting electricity theft, which is much better than others.

scholarly journals Research on Data Security and Privacy Protection of Wearable Equipment in Healthcare

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Dawei Jiang ◽  
Guoquan Shi
Keyword(s):  
Data Security ◽  
Privacy Protection ◽  
User Behavior ◽  
Legal Aspects ◽  
Wearable Devices ◽  
Security And Privacy ◽  
Time Data ◽  
User Privacy ◽  
Medical Network ◽  
User Data

With the close integration of science and technology and health, the broad application prospects of healthy interconnection bring revolutionary changes to health services. Health and medical wearable devices can collect real-time data related to user health, such as user behavior, mood, and sleep, which have great commercial and social value. Healthcare wearable devices, as important network nodes for health interconnection, connect patients and hospitals with the Internet of Things and sensing technology to form a huge medical network. As wearable devices can also collect user data regardless of time and place, uploading data to the cloud can easily make the wearable device’s system vulnerable to attacks and data leakage. Defects in technology can sometimes cause problems such as lack of control over data flow links in wearable devices, and data and privacy leaks are more likely to occur. In this regard, how to ensure the data security and user privacy while using healthcare wearable devices to collect data is a problem worth studying. This article investigates data from healthcare wearable devices, from technical, management, and legal aspects, and studies data security and privacy protection issues for healthcare wearable devices to protect data security and user privacy and promote the sustainable development of the healthcare wearable device industry and the scientific use of data collection.

An Efficient Privacy-Preserving Attribute-Based Encryption with Hidden Policy for Cloud Storage

2019 ◽  
Vol 28 (11) ◽  
pp. 1950186
Author(s):  
Chanying Huang ◽  
Songjie Wei ◽  
Anmin Fu
Keyword(s):  
Access Control ◽  
Data Security ◽  
Privacy Protection ◽  
Cloud Storage ◽  
Privacy Preserving ◽  
Equivalent Transformation ◽  
Secret Key ◽  
Cloud Data ◽  
Attribute Based Encryption ◽  
Key Length

Cloud storage is one of the most widely-used storage services, because it can provide users with unlimited, scalable, low-cost and convenient resource services. When data is outsourced to cloud for storage, data security and access control are the two essential issues that need to be addressed. Attribute-based encryption (ABE) scheme can provide sufficient data security and fine-grained access control for cloud data. As more and more attention is drawn to privacy protection, privacy preservation becomes another urgent issue for cloud storage. In ABE, since the access policies are generally stored in clear text, it will lead to the disclosure of users’ privacy. Some works sacrifice computational efficiency, key length or ciphertext size for privacy concerns. To solve these problems, this paper proposes an efficient privacy-preserving attribute-based encryption scheme with hidden policy for outsourced data. Using the idea of Boolean equivalent transformation, the proposed scheme achieves fast encryption and privacy protection for both data owner and legitimate visitors. In addition, the proposed scheme can satisfy constant secret key length and reasonable size of ciphertext requirements. We also conduct theoretical security analysis, and carry out experiments to prove that the proposed scheme has good performance in terms of computation, communication and storage overheads.

scholarly journals Privacy-Preserving Internet of Things: Techniques and Applications

2019 ◽  
Vol 8 (6) ◽  
pp. 3229-3234 ◽  
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Multiparty Computation ◽  
Iot Applications ◽  
Attribute Based Encryption ◽  
Daily Behavior ◽  
Iot Devices

Privacy has become an imperative term in the recent technology developments. Lots of data are being collected through every digital activity of users. The expeditious development of IoT applications have raised the concern about the privacy of the IoT systems. The data collected via IoT sensors can reveal the daily behavior of the users, location, and other sensitive information. Hence, it is necessary to preserve the privacy of data collected by IoT devices. A large number of techniques and approaches have been implemented and used in different IoT based applications such as cloud computing based IoT, fog computing based IoT, blockchain based IoT and trajectory applications. In this paper, we present a detailed investigation of the existing approaches to preserve the privacy of data in IoT applications. The techniques like k-anonymity, secure multiparty computation, attribute based encryption and homomorphic encryption are analyzed. Finally, a comparative analysis of privacy preserving techniques with its applications are presented.

scholarly journals Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation

2021 ◽  
Vol 3 (3) ◽  
pp. 250-262
Author(s):  
Jennifer S. Raj
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Privacy Preservation ◽  
Keyword Search ◽  
Power Saving ◽  
Privacy Preserving ◽  
Mobile Social Networks ◽  
User Privacy ◽  
Attribute Based Encryption ◽  
Ciphertext Policy

Several subscribing and content sharing services are largely personalized with the growing use of mobile social media technology. The end user privacy in terms of social relationships, interests and identities as well as shared content confidentiality are some of the privacy concerns in such services. The content is provided with fine-grained access control with the help of attribute-based encryption (ABE) in existing work. Decryption of privacy preserving content suffers high consumption of energy and data leakage to unauthorized people is faced when mobile social networks share privacy preserving data. In the mobile social networks, a secure proxy decryption model with enhanced publishing and subscribing scheme is presented in this paper as a solution to the aforementioned issues. The user credentials and data confidentiality are protected by access control techniques that work on privacy preserving in a self-contained manner. Keyword search based public-key encryption with ciphertext policy attribute-based encryption is used in this model. At the end users, ciphertext decryption is performed to reduce the energy consumption by the secure proxy decryption scheme. The effectiveness and efficiency of the privacy preservation model is observed from the experimental results.

scholarly journals Decentralised, privacy-preserving inference for mobile phone contact tracing of SARS-CoV-2

2020 ◽  
Author(s):  
Daniel Tang
Keyword(s):  
Bayesian Inference ◽  
Message Passing ◽  
Risk Model ◽  
Smart Phone ◽  
Privacy Preserving ◽  
Contact Tracing ◽  
Bayesian Posterior Probability ◽  
Sensitive Information ◽  
Test Results ◽  
User Privacy

With the announcement of Apple and Google's partnership to introduce contact-tracing functionality to iOS and Android, it seems increasingly likely that contact tracing via a smart-phone will form an important part of the effort to manage the COVID-19 pandemic and prevent resurgences of the disease after the initial outbreak.When deciding whether a person should be isolated, tested or released, information about test results and symptoms of that person's contacts should be used in the most efficient way to inform the decision. However, the privacy preserving nature of the Apple/Google contact tracing algorithm means that centralised curation of these decisions is not possible so each phone must use its own ``risk model'' to inform decisions. Ideally, the risk model should use Bayesian inference to decide the best course of action given the test results of the user and those of other users. Here we present a decentralised algorithm that estimates the Bayesian posterior probability of viral transmission events and evaluates when a user should be notified, tested or released from isolation while preserving user privacy. The algorithm also uses data from the contact tracing activity to be used to update the disease models on all phones and to allow Epidemiologists to better understand the dynamics of the disease.The algorithm is a message passing algorithm, based on belief propagation, so each smart-phone can be used to execute a small part of the algorithm without releasing any sensitive information. In this way, the network of all participating smart-phones forms a distributed computation device that performs Bayesian inference and informs each user when they should start/end isolation or be tested.

scholarly journals A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

2021 ◽  
Author(s):  
Fengmei Jin ◽  
Wen Hua ◽  
Matteo Francia ◽  
Pingfu Chao ◽  
Maria Orlowska ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Traffic Management ◽  
Real Life ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Data Publishing ◽  
Sensitive Information ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
The Right

<div>Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata.</div>

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