Modeling Analysis of Network Spatial Sensitive Information Detection Driven by Big Data

2019 ◽  
pp. 1-11
Author(s):  
Ruijuan Liu ◽  
Bin Yang ◽  
Shuai Liu
Keyword(s):  
Big Data ◽  
Sensitive Information ◽  
Modeling Analysis ◽  
Information Detection

scholarly journals Efficient and Privacy-Preserving Multi-User Outsourced K-Means Clustering

2021 ◽  
Vol 14 (2) ◽  
pp. 26
Author(s):  
Na Li ◽  
Lianguan Huang ◽  
Yanling Li ◽  
Meng Sun
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Locality Sensitive Hashing ◽  
Sensitive Information ◽  
The Public ◽  
Big Data Mining ◽  
Euclidean Distances ◽  
Computational Resources

In recent years, with the development of the Internet, the data on the network presents an outbreak trend. Big data mining aims at obtaining useful information through data processing, such as clustering, clarifying and so on. Clustering is an important branch of big data mining and it is popular because of its simplicity. A new trend for clients who lack of storage and computational resources is to outsource the data and clustering task to the public cloud platforms. However, as datasets used for clustering may contain some sensitive information (e.g., identity information, health information), simply outsourcing them to the cloud platforms can't protect the privacy. So clients tend to encrypt their databases before uploading to the cloud for clustering. In this paper, we focus on privacy protection and efficiency promotion with respect to k-means clustering, and we propose a new privacy-preserving multi-user outsourced k-means clustering algorithm which is based on locality sensitive hashing (LSH). In this algorithm, we use a Paillier cryptosystem encrypting databases, and combine LSH to prune off some unnecessary computations during the clustering. That is, we don't need to compute the Euclidean distances between each data record and each clustering center. Finally, the theoretical and experimental results show that our algorithm is more efficient than most existing privacy-preserving k-means clustering.

scholarly journals Big Data-Driven Cellular Information Detection and Coverage Identification

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 937 ◽  
Author(s):  
Hai Wang ◽  
Su Xie ◽  
Ke Li ◽  
M. Ahmad
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Network Optimization ◽  
Base Station ◽  
Data Driven ◽  
Related Data ◽  
Massive Number ◽  
Information Detection ◽  
Conventional Methods ◽  
Wireless Network Optimization

As one of the core data assets of telecom operators, base station almanac (BSA) plays an important role in the operation and maintenance of mobile networks. It is also an important source of data for the location-based service (LBS) providers. However, it is always less timely updated, nor it is accurate enough. Besides, it is not open to third parties. Conventional methods detect only the location of the base station (BS) which cannot satisfy the needs of network optimization and maintenance. Because of these drawbacks, in this paper, a big-data driven method of BSA information detection and cellular coverage identification is proposed. With the help of network-related data crowd sensed from the massive number of smartphone users in the live network, the algorithm can estimate more parameters of BSA with higher accuracy than conventional methods. The coverage capability of each cell was also identified in a granularity of small geographical grids. Computational results validate the proposed algorithm with higher performance and detection ability over the existing ones. The new method can be expected to improve the scope, accuracy, and timeliness of BSA, serving for wireless network optimization and maintenance as well as LBS service.

Optimal Privacy Preserving Technique Over Big Data Analytics Using Oppositional Fruit Fly Algorithm

2020 ◽  
Vol 13 (2) ◽  
pp. 283-295
Author(s):  
Ajmeera Kiran ◽  
Vasumathi Devara
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Input Data ◽  
Clustering Algorithm ◽  
Big Data Analytics ◽  
Fruit Fly ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Convolution Process ◽  
Fuzzy C Means Clustering

Background: Big data analytics is the process of utilizing a collection of data accompanied on the internet to store and retrieve anywhere and at any time. Big data is not simply a data but it involves the data generated by variety of gadgets or devices or applications. Objective: When massive volume of data is stored, there is a possibility for malevolent attacks on the searching data are stored in the server because of under privileged privacy preserving approaches. These traditional methods result in many drawbacks due to various attacks on sensitive information. Hence, to enhance the privacy preserving for sensitive information stored in the database, the proposed method makes use of efficient methods. Methods: In this manuscript, an optimal privacy preserving over big data using Hadoop and mapreduce framework is proposed. Initially, the input data is grouped by modified fuzzy c means clustering algorithm. Then we are performing a map reduce framework. And then the clustered data is fed to the mapper; in mapper the privacy of input data is done by convolution process. To validate the privacy of input data the recommended technique utilizes the optimal artificial neural network. Here, oppositional fruit fly algorithm is used to enhancing the neural networks. Results: The routine of the suggested system is assessed by means of clustering accuracy, error value, memory, and time. The experimentation is performed by KDD dataset. Conclusion: A result shows that our proposed system has maximum accuracy and attains the effective convolution process to improve privacy preserving.

De-Identification of Health Data in Big Data using a Novel Bio-Inspired Apoptosis Algorithm

2015 ◽  
Vol 5 (3) ◽  
pp. 1-15
Author(s):  
Amine Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Privacy Concerns ◽  
Shared Data ◽  
Data Mining Algorithms ◽  
Identity Disclosure ◽  
Using Data ◽  
Mining Algorithms

In the last years, with the emergence of new technologies in the image of big data, the privacy concerns had grown widely. However, big data means the dematerialization of the data. The classical security solutions are no longer efficient in this case. Nowadays, sharing the data is much easier as well as saying hello. The amount of shared data over the web keeps growing from day to another which creates a wide gap between the purpose of sharing data and the fact that these last contain sensitive information. For that, the researches turned their attention to new issues and domains in order to minimize this gap. In other way, they intended to ensure a good utility of data by preserving its meaning while hiding sensitive information to prevent identity disclosure. Many techniques had been used for that. Some of it is mathematical and other ones using data mining algorithms. This paper deals with the problem of hiding sensitive data in shared structured medical data using a new bio-inspired algorithm from the natural phenomena of apoptosis cells in human body.

scholarly journals Automating Open Science for Big Data

2015 ◽  
Vol 659 (1) ◽  
pp. 260-273 ◽  
Author(s):  
Mercè Crosas ◽  
Gary King ◽  
James Honaker ◽  
Latanya Sweeney
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Science Research ◽  
Science Research ◽  
Open Science ◽  
Streaming Data ◽  
Sensitive Information ◽  
Increased Risk ◽  
The Social ◽  
New Challenges

The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher’s computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. The trend toward big data—including large-scale streaming data—is starting to transform research and has the potential to impact policymaking as well as our understanding of the social, economic, and political problems that affect human societies. However, big data research poses new challenges to the execution of the analysis, archiving and reuse of the data, and reproduction of the results. Downloading these datasets to a researcher’s computer is impractical, leading to analyses taking place in the cloud, and requiring unusual expertise, collaboration, and tool development. The increased amount of information in these large datasets is an advantage, but at the same time it poses an increased risk of revealing personally identifiable sensitive information. In this article, we discuss solutions to these new challenges so that the social sciences can realize the potential of big data.

Risks, Security, and Privacy for HIV/AIDS Data

2019 ◽  
pp. 58-74 ◽  
Author(s):  
Md Tarique Jamal Ansari ◽  
Dhirendra Pandey
Keyword(s):  
Big Data ◽  
Health Information ◽  
Information Exchange ◽  
Sensitive Information ◽  
Coordinated Care ◽  
Aids Patients ◽  
Privacy And Security ◽  
Prevention And Treatment ◽  
Legal Frameworks ◽  
Hiv Aids

Big data has the potential to transform healthcare systems for the prevention and treatment of HIV/AIDS by providing analytic tools that are capable of handling huge and different types of data at very fast speeds. Big data's transformative potential is also introverted by privacy and security requirements for HIV/AIDS patients' sensitive data that restrict health information exchange. Electronic health records provide the opportunity for HIV/AIDS patients to receive improved coordinated care from healthcare providers and easier access to their health information. This chapter discusses the various legal frameworks governing health information, dispels misconceptions about privacy regulations, and highlights how these legal frameworks provide privacy, confidentiality, and security to this sensitive information, and shows how EHRs can maximize the utility of big data to improve HIV/AIDS prevention and treatment.

Bootstrapping Urban Planning

2020 ◽  
pp. 217-246
Author(s):  
Ankur Lohachab
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Sensitive Information ◽  
Ongoing Research ◽  
Research Issues ◽  
Advantages And Disadvantages ◽  
Efficient Management ◽  
Different Characteristics

Rapid growth of embedded devices and population density in IoT-based smart cities provides great potential for business and opportunities in urban planning. For addressing the current and future needs of living, smart cities have to revitalize the potential of big data analytics. However, a colossal amount of sensitive information invites various computational challenges. Moreover, big data generated by the IoT paradigm acquires different characteristics as compared to traditional big data because it contains heterogeneous unstructured data. Despite various challenges in big data, enterprises are trying to utilize its true potential for providing proactive applications to the citizens. In this chapter, the author finds the possibilities of the role of big data in the efficient management of smart cities. Representative applications of big data, along with advantages and disadvantages, are also discussed. By delving into the ongoing research approaches in securing and providing privacy to big data, this chapter is concluded by highlighting the open research issues in the domain.

A Secure Protocol for High-Dimensional Big Data Providing Data Privacy

2021 ◽  
pp. 327-343
Author(s):  
Anitha J. ◽  
Prasad S. P.
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Personal Information ◽  
Technological Development ◽  
High Dimensional ◽  
Sensitive Information ◽  
Data Anonymization ◽  
Secure Protocol ◽  
Data Owner

Due to recent technological development, a huge amount of data generated by social networking, sensor networks, internet, etc., adds more challenges when performing data storage and processing tasks. During PPDP, the collected data may contain sensitive information about the data owner. Directly releasing this for further processing may violate the privacy of the data owner, hence data modification is needed so that it does not disclose any personal information. The existing techniques of data anonymization have a fixed scheme with a small number of dimensions. There are various types of attacks on the privacy of data like linkage attack, homogeneity attack, and background knowledge attack. To provide an effective technique in big data to maintain data privacy and prevent linkage attacks, this paper proposes a privacy preserving protocol, UNION, for a multi-party data provider. Experiments show that this technique provides a better data utility to handle high dimensional data, and scalability with respect to the data size compared with existing anonymization techniques.

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