scholarly journals Searchable Encryption Scheme for Personalized Privacy in IoT-Based Big Data

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1059 ◽  
Author(s):  
Shuai Li ◽  
Miao Li ◽  
Haitao Xu ◽  
Xianwei Zhou
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Physical World ◽  
Research Area ◽  
Searchable Encryption ◽  
Encryption Scheme ◽  
Key Issues ◽  
Intelligent Devices ◽  
Active Research

The Internet of things (IoT) has become a significant part of our daily life. Composed of millions of intelligent devices, IoT can interconnect people with the physical world. With the development of IoT technology, the amount of data generated by sensors or devices is increasing dramatically. IoT-based big data has become a very active research area. One of the key issues in IoT-based big data is ensuring the utility of data while preserving privacy. In this paper, we deal with the protection of big data privacy in the data storage phase and propose a searchable encryption scheme satisfying personalized privacy needs. Our proposed scheme works for all file types including text, audio, image, video, etc., and meets different privacy needs of different individuals at the expense of high storage cost. We also show that our proposed scheme satisfies index indistinguishability and trapdoor indistinguishability.

scholarly journals Enhancing Big Data Auditing

2018 ◽  
Vol 11 (1) ◽  
pp. 90
Author(s):  
Sara Alomari ◽  
Mona Alghamdi ◽  
Fahd S. Alotaibi
Keyword(s):  
Big Data ◽  
Research Area ◽  
Provable Data Possession ◽  
Integrity Verification ◽  
The Core ◽  
Outsourced Data ◽  
Active Research ◽  
Data Auditing ◽  
Active Research Area ◽  
Proof Of Retrievability

The auditing services of the outsourced data, especially big data, have been an active research area recently. Many schemes of remotely data auditing (RDA) have been proposed. Both categories of RDA, which are Provable Data Possession (PDP) and Proof of Retrievability (PoR), mostly represent the core schemes for most researchers to derive new schemes that support additional capabilities such as batch and dynamic auditing. In this paper, we choose the most popular PDP schemes to be investigated due to the existence of many PDP techniques which are further improved to achieve efficient integrity verification. We firstly review the work of literature to form the required knowledge about the auditing services and related schemes. Secondly, we specify a methodology to be adhered to attain the research goals. Then, we define each selected PDP scheme and the auditing properties to be used to compare between the chosen schemes. Therefore, we decide, if possible, which scheme is optimal in handling big data auditing.

Searchable Encryption System for Big Data Storage

2021 ◽  
pp. 139-150
Author(s):  
Yuxiang Chen ◽  
Yao Hao ◽  
Zhongqiang Yi ◽  
Kaijun Wu ◽  
Qi Zhao ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Searchable Encryption ◽  
Big Data Storage

scholarly journals A Survey on Job Scheduling in Big Data

2016 ◽  
Vol 16 (3) ◽  
pp. 35-51 ◽  
Author(s):  
M. Senthilkumar ◽  
P. Ilango
Keyword(s):  
Big Data ◽  
Job Scheduling ◽  
Research Area ◽  
Data Locality ◽  
Distributed Data ◽  
Distributed Data Processing ◽  
Big Data Applications ◽  
Mapreduce Model ◽  
Active Research ◽  
And Behavior

Abstract Big Data Applications with Scheduling becomes an active research area in last three years. The Hadoop framework becomes very popular and most used frameworks in a distributed data processing. Hadoop is also open source software that allows the user to effectively utilize the hardware. Various scheduling algorithms of the MapReduce model using Hadoop vary with design and behavior, and are used for handling many issues like data locality, awareness with resource, energy and time. This paper gives the outline of job scheduling, classification of the scheduler, and comparison of different existing algorithms with advantages, drawbacks, limitations. In this paper, we discussed various tools and frameworks used for monitoring and the ways to improve the performance in MapReduce. This paper helps the beginners and researchers in understanding the scheduling mechanisms used in Big Data.

scholarly journals A parallelized disjunctive query based searchable encryption scheme for big data

2020 ◽  
Vol 109 ◽  
pp. 583-592 ◽  
Author(s):  
Shahzaib Tahir ◽  
Liutauras Steponkus ◽  
Sushmita Ruj ◽  
Muttukrishnan Rajarajan ◽  
Ali Sajjad
Keyword(s):  
Big Data ◽  
Searchable Encryption ◽  
Encryption Scheme

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.

Epilogue

2019 ◽  
pp. 128-130
Author(s):  
Richard Earl
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Research Area ◽  
General Topology ◽  
Topological Data Analysis ◽  
Data Sets ◽  
Current Interest ◽  
And Topology ◽  
Active Research ◽  
Active Research Area

Topology remains a large, active research area in mathematics. Unsurprisingly its character has changed over the last century—there is considerably less current interest in general topology, but whole new areas have emerged, such as topological data analysis to help analyze big data sets. The Epilogue concludes that the interfaces of topology with other areas have remained rich and numerous, and it can be hard telling where topology stops and geometry or algebra or analysis or physics begin. Often that richness comes from studying structures that have interconnected flavours of algebra, geometry, and topology, but sometimes a result, seemingly of an entirely algebraic nature say, can be proved by purely topological means.

scholarly journals Research on Big data Security privacy Protection based on cloud Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042077
Author(s):  
Tongtong Xu ◽  
Lei Shi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Security ◽  
Privacy Protection ◽  
Data Privacy ◽  
Cloud Services ◽  
Security Mechanism ◽  
Computing Platform ◽  
Cloud Servers

Abstract Cloud computing is a new way of computing and storage. Users do not need to master professional skills, but can enjoy convenient network services as long as they pay according to their own needs. When we use cloud services, we need to upload data to cloud servers. As the cloud is an open environment, it is easy for attackers to use cloud computing to conduct excessive computational analysis on big data, which is bound to infringe on others’ privacy. In this process, we inevitably face the challenge of data security. How to ensure data privacy security in the cloud environment has become an urgent problem to be solved. This paper studies the big data security privacy protection based on cloud computing platform. This paper starts from two aspects: implicit security mechanism and display security mechanism (encryption mechanism), so as to protect the security privacy of cloud big data platform in data storage and data computing processing.

scholarly journals PROTEKSI PRIVASI BIG DATA DALAM MEDIA SOSIAL

2020 ◽  
Vol 3 (1) ◽  
pp. 1-33
Author(s):  
Winarsih Winarsih ◽  
Irwansyah Irwansyah
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Service Providers ◽  
Meta Analysis ◽  
Privacy Management ◽  
Communication Privacy Management ◽  
The Public ◽  
Privacy Issues

AbstrakPerkembangan media sosial di Indonesia begitu pesat dengan jumlah pengguna yang  terus  meningkat.   Akan   tetapi  hal  tersebut   kurang  diimbangi   dengan kesadaran tentang privasi dalam kaitannya dengan big data yang dihasilkan oleh penyedia  layanan.  Penyedia  layanan  memberikan  kebijakan  berupa  syarat dan ketentuan  akan tetapi masyarakat  umumnya masih rendah dalam hal memiliki kesadaran  tentang privasi  data pribadi  mereka.  Penelitian  ini bertujuan  untuk mengetahui  solusi dari permasalahan  privasi  big data  dalam  media  sosial  dan dianalisis   dengan   teori  privasi   komunikasi.   Metode  yang  digunakan   dalam penelitian ini adalah metode meta-analisis yang mengolah hasil temuan dari penelitian sebelumnya. Hasil dari penelitian ini berupa solusi bagi perlindungan privasi data individu saat pembuatan, penyimpanan, dan pemrosesan data. Kata Kunci: data besar, Indonesia, kebijakan, media sosial, privasi AbstractThe development of social media in Indonesia is high increasing. However, this is not  accompanied   by  awareness   of  privacy  in  its  commitment   to  big  data generated  by service providers.  The service provider provides an agreed policy, will provide the public about their data privacy issues. This article used Communication Privacy Management to finding solution about big data privacy problems.   The  method  used  in  this  study  is  a  meta-analysis   method   that processes  the findings  from previous  studies.  The results  of this study contain solutions for privacy protection when creating data, data storage, and processing data. Keywords: big data, Indonesia, policy, social media, privacy

An efficient and forward-secure lattice-based searchable encryption scheme for the Big-data era

2021 ◽  
Vol 96 ◽  
pp. 107533
Author(s):  
SK Hafizul Islam ◽  
Nimish Mishra ◽  
Souvik Biswas ◽  
Bharat Keswani ◽  
Sherali Zeadally
Keyword(s):  
Big Data ◽  
Searchable Encryption ◽  
Encryption Scheme

Blockchain-Based Data Market (BCBDM) Framework for Security and Privacy

2021 ◽  
pp. 186-205
Author(s):  
Shailesh Pancham Khapre ◽  
Chandramohan Dhasarathan ◽  
Puviyarasi T. ◽  
Sam Goundar
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Data Storage ◽  
Data Privacy ◽  
Security And Privacy ◽  
Research Progress ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Data Market ◽  
Privacy Issues

In the internet era, incalculable data is generated every day. In the process of data sharing, complex issues such as data privacy and ownership are emerging. Blockchain is a decentralized distributed data storage technology. The introduction of blockchain can eliminate the disadvantages of the centralized data market, but at the same time, distributed data markets have created security and privacy issues. It summarizes the industry status and research progress of the domestic and foreign big data trading markets and refines the nature of the blockchain-based big data sharing and circulation platform. Based on these properties, a blockchain-based data market (BCBDM) framework is proposed, and the security and privacy issues as well as corresponding solutions in this framework are analyzed and discussed. Based on this framework, a data market testing system was implemented, and the feasibility and security of the framework were confirmed.

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