scholarly journals Personal Privacy Data Protection in Location Recommendation System

2021 ◽  
Vol 2138 (1) ◽  
pp. 012026
Author(s):  
Linrui Han
Keyword(s):  
Data Storage ◽  
Data Protection ◽  
Data Security ◽  
Recommendation System ◽  
Data Availability ◽  
Data Publishing ◽  
Published Data ◽  
Personal Privacy ◽  
Location Recommendation ◽  
Privacy Issues

Abstract At present, there are many location-based recommendation algorithms and systems, including location calculation, route calculation, and so on. However, in the general information data publishing, the privacy issues in the published data have not been fully paid attention to and protected. The purpose of this article is to investigate the effectiveness of personal privacy data protection in location recommendation systems. This paper first introduces the basis and importance of research on data security and secrecy, analyses personal privacy issues in data publishing in the era of big data, summarizes the research status in the field of security and secrecy at home and abroad, and introduces the process of data security and the role of users in it. Then, some classic privacy security modules in this field are introduced, and the privacy of data storage security concepts in the current situation mentioned in this paper is analyzed. A geographic location-based privacy protection scheme in mobile cloud is proposed. Privacy analysis, sensitive attribute generalization information analysis, route synthesis analysis and related experiments are performed on the location recommendation system. The experimental results show that the scheme proposed in this paper is more secure and has less loss of data availability.

scholarly journals A Survey on Data Security in Cloud Computing Using Blockchain: Challenges, Existing-State-Of-The-Art Methods, And Future Directions

2021 ◽  
Vol 5 (3) ◽  
pp. 15-30
Author(s):  
Muhammad Usman Ashraf
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Security ◽  
Data Dissemination ◽  
State Of The Art ◽  
Storage System ◽  
Research Work ◽  
Detailed Comparison ◽  
Data Availability ◽  
Art Methods

Cloud computing is one of the ruling storage solutions. However, the cloud computing centralized storage method is not stable. Blockchain, on the other hand, is a decentralized cloud storage system that ensures data security. Cloud environments are vulnerable to several attacks which compromise the basic confidentiality, integrity, availability, and security of the network. This research focus on decentralized, safe data storage, high data availability, and effective use of storage resources. To properly respond to the situation of the blockchain method, we have conducted a comprehensive survey of the most recent and promising blockchain state-of-the-art methods, the P2P network for data dissemination, hash functions for data authentication, and IPFS (InterPlanetary File System) protocol for data integrity. Furthermore, we have discussed a detailed comparison of consensus algorithms of Blockchain concerning security. Also, we have discussed the future of blockchain and cloud computing. The major focus of this study is to secure the data in Cloud computing using blockchain and ease for researchers for further research work.

Privacy Preserving Anonymity for Periodical Releases of Spontaneous ADE Reporting Data: Algorithm Development and Validation (Preprint)

2021 ◽  
Author(s):  
Wen-Yang Lin ◽  
Jie-Teng Wang
Keyword(s):  
Adverse Drug Events ◽  
Personal Information ◽  
Data Publishing ◽  
Published Data ◽  
Sensitive Information ◽  
Personal Privacy ◽  
Data Utility ◽  
Data Anonymization ◽  
Privacy Model ◽  
Bounding Model

BACKGROUND Increasingly, spontaneous reporting systems (SRS) have been established to collect adverse drug events to foster the research of ADR detection and analysis. SRS data contains personal information and so its publication requires data anonymization to prevent the disclosure of individual privacy. We previously have proposed a privacy model called MS(k, θ*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically, e.g., FAERS, to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, i.e., MS(k, θ*)-bounding. OBJECTIVE We investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintain the utility of published data. METHODS We identify some potential attacks on periodical releases of SRS data, namely BFL-attacks, that are mainly caused by follow-up cases. We present a new privacy model called PPMS(k, θ*)-bounding, and propose the associated PPMS-Anonymization algorithm along with two improvements, PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter datasets, from 2004Q1 to 2011Q4. The performance of the proposed three versions of PPMS-Anonymization were inspected against MS-Anonymization from some aspects, including data distortion, measured by Normalized Information Loss (NIS); privacy risk of anonymized data, measured by Dangerous Identity Ratio (DIR) and Dangerous Sensitivity Ratio (DSR); and data utility, measured by bias of signal counting and strength (PRR). RESULTS The results show that our new method can prevent privacy disclosure for periodical releases of SRS data with reasonable sacrifice of data utility and acceptable deviation of the strength of ADR signals. The best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization both in privacy protection and data utility. CONCLUSIONS The proposed PPMS(k, θ*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS datasets in the periodical data publishing scenario, preventing the series of releases from the disclosure of personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection.

scholarly journals Data security and integrity in cloud computing : Threats and Solutions

2020 ◽  
pp. 356-363
Author(s):  
Rajesh Keshavrao Sadavarte ◽  
G. D. Kurundkar
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Protection ◽  
Data Security ◽  
Key Management ◽  
Cost Effective ◽  
Third Party ◽  
The World ◽  
The Times ◽  
Few Data

Cloud computing changed the world of Internet. With the help of cloud computing user can easily share, store and retrieve their data from anywhere. Cloud computing is scalable, fast, flexible, and cost-effective technology platform for IT enabled services over the internet. Cloud computing provides hardware, software and infrastructural storage to many users at a time Most of the times cloud users don’t know the exact location of their data or the sources of data stored with their data. In spite of various benefits that are provided by the cloud computing services, its users are very much afraid about the security of their data once it is over the cloud under the control of third-party vendors. Therefore, many data security and integrity concerns like access control, searchable encryption techniques, key management, ownership proofs and remote integrity check arise. This paper discusses the security and integrity of data in cloud computing. It is a study of aspects related to data security. The paper will go in to details of few data protection methods and approaches used throughout the world to ensure maximum data protection by reducing risks and threats. Going ahead the paper also discuss different techniques used for secured data storage on cloud.

scholarly journals Towards Achieving Personal Privacy Protection and Data Security on Integrated E-Voting Model of Blockchain and Message Queue

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Siriboon Chaisawat ◽  
Chalee Vorakulpipat
Keyword(s):  
Data Storage ◽  
Data Security ◽  
Data Privacy ◽  
Performance Tests ◽  
Production Environment ◽  
Personal Privacy ◽  
Integration Model ◽  
Message Queue ◽  
Blockchain Technology ◽  
New Challenges

The growing number of e-voting applications indicates the need in resolving issues that exist in the traditional election model. By integrating with blockchain technology, we could extend the model’s capabilities by presenting transparency in logic execution and integrity in data storage. Despite these advantages, blockchain brings in new challenges regarding system performance and data privacy. Due to distributed nature of blockchain, any new updating request needs to be reflected in all network’s peers before proceeding to the subsequence requests. This process produces delay and possibility in request rejection due to update conflict. In addition, data removal is no longer feasible since each record is protected by immutable hashed link. To overcome these limitations, the integration model of blockchain and message queue is proposed in this paper. The design addresses security concerns in data exchanging patterns, voter anonymization, and proof of system actor’s legitimacy. Performance tests are conducted on system prototypes which were deployed on two different settings. The result shows that the system can perform well in production environment, and introduction of message queue handling scheme can cope with blockchain’s errors in unexpected scenarios.

Anonymization Based on Improved Bucketization (AIB): A Privacy-Preserving Data Publishing Technique for Improving Data Utility in Healthcare Data

2021 ◽  
Vol 11 (12) ◽  
pp. 3164-3173
Author(s):  
R. Indhumathi ◽  
S. Sathiya Devi
Keyword(s):  
Medical Information ◽  
Threshold Value ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Published Data ◽  
Sensitive Information ◽  
Data Utility ◽  
Healthcare Data ◽  
Privacy Preserving Data Publishing ◽  
Horizontal Partitioning

Data sharing is essential in present biomedical research. A large quantity of medical information is gathered and for different objectives of analysis and study. Because of its large collection, anonymity is essential. Thus, it is quite important to preserve privacy and prevent leakage of sensitive information of patients. Most of the Anonymization methods such as generalisation, suppression and perturbation are proposed to overcome the information leak which degrades the utility of the collected data. During data sanitization, the utility is automatically diminished. Privacy Preserving Data Publishing faces the main drawback of maintaining tradeoff between privacy and data utility. To address this issue, an efficient algorithm called Anonymization based on Improved Bucketization (AIB) is proposed, which increases the utility of published data while maintaining privacy. The Bucketization technique is used in this paper with the intervention of the clustering method. The proposed work is divided into three stages: (i) Vertical and Horizontal partitioning (ii) Assigning Sensitive index to attributes in the cluster (iii) Verifying each cluster against privacy threshold (iv) Examining for privacy breach in Quasi Identifier (QI). To increase the utility of published data, the threshold value is determined based on the distribution of elements in each attribute, and the anonymization method is applied only to the specific QI element. As a result, the data utility has been improved. Finally, the evaluation results validated the design of paper and demonstrated that our design is effective in improving data utility.

scholarly journals A New Secure Model for Data Protection over Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Amr M. Sauber ◽  
Passent M. El-Kafrawy ◽  
Amr F. Shawish ◽  
Mohamed A. Amin ◽  
Ismail M. Hagag
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Protection ◽  
Cloud Services ◽  
Security And Privacy ◽  
Storage Model ◽  
Cloud Data ◽  
Security Issues ◽  
Data Owner ◽  
Proposed Model

The main goal of any data storage model on the cloud is accessing data in an easy way without risking its security. A security consideration is a major aspect in any cloud data storage model to provide safety and efficiency. In this paper, we propose a secure data protection model over the cloud. The proposed model presents a solution to some security issues of cloud such as data protection from any violations and protection from a fake authorized identity user, which adversely affects the security of the cloud. This paper includes multiple issues and challenges with cloud computing that impairs security and privacy of data. It presents the threats and attacks that affect data residing in the cloud. Our proposed model provides the benefits and effectiveness of security in cloud computing such as enhancement of the encryption of data in the cloud. It provides security and scalability of data sharing for users on the cloud computing. Our model achieves the security functions over cloud computing such as identification and authentication, authorization, and encryption. Also, this model protects the system from any fake data owner who enters malicious information that may destroy the main goal of cloud services. We develop the one-time password (OTP) as a logging technique and uploading technique to protect users and data owners from any fake unauthorized access to the cloud. We implement our model using a simulation of the model called Next Generation Secure Cloud Server (NG-Cloud). These results increase the security protection techniques for end user and data owner from fake user and fake data owner in the cloud.

scholarly journals Privacy protection of medical data in social network

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Jie Su ◽  
Yi Cao ◽  
Yuehui Chen ◽  
Yahui Liu ◽  
Jinming Song
Keyword(s):  
Social Network ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Medical Data ◽  
Research Field ◽  
Data Availability ◽  
Published Data ◽  
Institute Of Medicine ◽  
The Usa ◽  
Medical Dataset

Abstract Background Protection of privacy data published in the health care field is an important research field. The Health Insurance Portability and Accountability Act (HIPAA) in the USA is the current legislation for privacy protection. However, the Institute of Medicine Committee on Health Research and the Privacy of Health Information recently concluded that HIPAA cannot adequately safeguard the privacy, while at the same time researchers cannot use the medical data for effective researches. Therefore, more effective privacy protection methods are urgently needed to ensure the security of released medical data. Methods Privacy protection methods based on clustering are the methods and algorithms to ensure that the published data remains useful and protected. In this paper, we first analyzed the importance of the key attributes of medical data in the social network. According to the attribute function and the main objective of privacy protection, the attribute information was divided into three categories. We then proposed an algorithm based on greedy clustering to group the data points according to the attributes and the connective information of the nodes in the published social network. Finally, we analyzed the loss of information during the procedure of clustering, and evaluated the proposed approach with respect to classification accuracy and information loss rates on a medical dataset. Results The associated social network of a medical dataset was analyzed for privacy preservation. We evaluated the values of generalization loss and structure loss for different values of k and a, i.e. $$k$$ k  = {3, 6, 9, 12, 15, 18, 21, 24, 27, 30}, a = {0, 0.2, 0.4, 0.6, 0.8, 1}. The experimental results in our proposed approach showed that the generalization loss approached optimal when a = 1 and k = 21, and structure loss approached optimal when a = 0.4 and k = 3. Conclusion We showed the importance of the attributes and the structure of the released health data in privacy preservation. Our method achieved better results of privacy preservation in social network by optimizing generalization loss and structure loss. The proposed method to evaluate loss obtained a balance between the data availability and the risk of privacy leakage.

scholarly journals Data ownership and data publishing

2019 ◽  
Vol 2 ◽  
Author(s):  
Lyubomir Penev
Keyword(s):  
Data Protection ◽  
Open Data ◽  
Data Publishing ◽  
Supplementary File ◽  
Biodiversity Data ◽  
Biodiversity Knowledge ◽  
Data Ownership ◽  
Data Hoarding ◽  
Data Elements ◽  
Access To Data

"Data ownership" is actually an oxymoron, because there could not be a copyright (ownership) on facts or ideas, hence no data onwership rights and law exist. The term refers to various kinds of data protection instruments: Intellectual Property Rights (IPR) (mostly copyright) asserted to indicate some kind of data ownership, confidentiality clauses/rules, database right protection (in the European Union only), or personal data protection (GDPR) (Scassa 2018). Data protection is often realised via different mechanisms of "data hoarding", that is witholding access to data for various reasons (Sieber 1989). Data hoarding, however, does not put the data into someone's ownership. Nonetheless, the access to and the re-use of data, and biodiversuty data in particular, is hampered by technical, economic, sociological, legal and other factors, although there should be no formal legal provisions related to copyright that may prevent anyone who needs to use them (Egloff et al. 2014, Egloff et al. 2017, see also the Bouchout Declaration). One of the best ways to provide access to data is to publish these so that the data creators and holders are credited for their efforts. As one of the pioneers in biodiversity data publishing, Pensoft has adopted a multiple-approach data publishing model, resulting in the ARPHA-BioDiv toolbox and in extensive Strategies and Guidelines for Publishing of Biodiversity Data (Penev et al. 2017a, Penev et al. 2017b). ARPHA-BioDiv consists of several data publishing workflows: Deposition of underlying data in an external repository and/or its publication as supplementary file(s) to the related article which are then linked and/or cited in-tex. Supplementary files are published under their own DOIs to increase citability). Description of data in data papers after they have been deposited in trusted repositories and/or as supplementary files; the systme allows for data papers to be submitted both as plain text or converted into manuscripts from Ecological Metadata Language (EML) metadata. Import of structured data into the article text from tables or via web services and their susequent download/distribution from the published article as part of the integrated narrative and data publishing workflow realised by the Biodiversity Data Journal. Publication of data in structured, semanticaly enriched, full-text XMLs where data elements are machine-readable and easy-to-harvest. Extraction of Linked Open Data (LOD) from literature, which is then converted into interoperable RDF triples (in accordance with the OpenBiodiv-O ontology) (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph Deposition of underlying data in an external repository and/or its publication as supplementary file(s) to the related article which are then linked and/or cited in-tex. Supplementary files are published under their own DOIs to increase citability). Description of data in data papers after they have been deposited in trusted repositories and/or as supplementary files; the systme allows for data papers to be submitted both as plain text or converted into manuscripts from Ecological Metadata Language (EML) metadata. Import of structured data into the article text from tables or via web services and their susequent download/distribution from the published article as part of the integrated narrative and data publishing workflow realised by the Biodiversity Data Journal. Publication of data in structured, semanticaly enriched, full-text XMLs where data elements are machine-readable and easy-to-harvest. Extraction of Linked Open Data (LOD) from literature, which is then converted into interoperable RDF triples (in accordance with the OpenBiodiv-O ontology) (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph In combination with text and data mining (TDM) technologies for legacy literature (PDF) developed by Plazi, these approaches show different angles to the future of biodiversity data publishing and, lay the foundations of an entire data publishing ecosystem in the field, while also supplying FAIR (Findable, Accessible, Interoperable and Reusable) data to several interoperable overarching infrastructures, such as Global Biodiversity Information Facility (GBIF), Biodiversity Literature Repository (BLR), Plazi TreatmentBank, OpenBiodiv, as well as to various end users.

Amelioration of Anonymity Modus Operandi for Privacy Preserving Data Publishing

2014 ◽  
pp. 96-107 ◽  
Author(s):  
J. Indumathi
Keyword(s):  
Performance Measures ◽  
Privacy Preserving ◽  
Research Community ◽  
Data Publishing ◽  
Modus Operandi ◽  
Privacy Preserving Data Mining ◽  
Research Problems ◽  
Privacy Preserving Data Publishing ◽  
Cryptographic Techniques ◽  
Privacy Issues

The scientific tumultuous intonation has swept our feet's, of its balance and at the same time wheedled us to reach the take-off arena from where we can march equipped and outfitted into the subsequent century with confidence & self-assurance; by unearthing solutions for all information security related issues (with special emphasis on privacy issues). Examining various outstanding research problems that encompass to be embarked upon for effectively managing and controlling the balance between privacy and utility, the research community is pressurized to propose suitable elucidations. The solution is to engender several Privacy-Preserving Data Publishing (PPDP) techniques like Perturbation, swapping, randomization, cryptographic techniques etc., Amongst the various available techniques k-anonymity is unique in facet of its association with protection techniques that preserve the truthfulness of the data. The principal chip in of this sketch out comprises: 1) Motivation for this exploration for Amelioration Of Anonymity Modus Operandi For Privacy Preserving Data Mining; 2) investigation of well-known research approaches to PPDM; 3) argue solutions to tackle the problems of security threats and attacks in the PPDM in systems; 4) related survey of the various anonymity techniques; 5) exploration of metrics for the diverse anonymity techniques; 6) performance measures for the various anonymity techniques; and 7) contradistinguish the diverse anonymity techniques and algorithms.

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