Efficient Security Framework for Sensitive Data Sharing and Privacy Preserving on Big-Data and Cloud Platforms

Pise, Priya Dudhale; Uke, Nilesh

doi:10.1145/2896387.2896423

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Technical network‐level issue: Cloud computing used unsecured networks like the internet for data communication; due to this insecurity of computer networks, each node can freely alter or internode communication so that to break the whole connection of the system 14,32–34 …”

Section: Literature Reviewmentioning

confidence: 99%

Identification and prioritization of security challenges of big data on cloud computing based on SLR: A fuzzy‐TOPSIS analysis approach

Khan

et al. 2021

J Software Evolu Process

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Nowadays, data is increasing exponentially, although cloud computing is an eminent approach for the organization, processing, and availability of data for organizational growth over the internet. Besides, a lot of advantages of cloud computing, yet it is suffered from security challenges, which affect big data while using cloud services on the internet. For this purpose, we conducted a detailed systematic literature review (SLR) study, to identify and capture the security challenges of big data on the cloud computing platform. Our research findings determine and develop a taxonomy, based on the prioritization of the security challenges of big data on cloud computing. We identified a total of 15 critical security challenges using the proposed SLR, with the frequency of each challenge >25%, and are further validated by the industrial specialists using a questionnaire survey study. The identified security challenges are data secrecy issue, geographical data location issue, unauthorized data access issue, lack of control, lack of data management, network‐level issues, data integrity issue, data recovery issue, lack of trust, data sharing issue, data availability, asset issues, legal amenabilities, lack of quality issues, and lack of consistency. The security challenges identified and captured through the SLR study are categorized into four levels, namely, steadiness, management, control, and eminence. Conclusively, we applied the fuzzy‐TOPSIS approach to prioritize and identify the significance of each identified security challenge for the big data usage on cloud computing. Based on our proposed approach, the “data secrecy issue” has been identified as the most prominent security challenge with the captured value of “0.765.” The fuzzy TOPSIS is an effective and innovative research approach in the field of computer science. It has been applied positively to other research areas to address and identify the fuzziness and uncertainty of multiple decision‐making glitches. The findings of the research paper will assist the software vendor organization when using the cloud platform for big data security. Also, using the proposed approach, software vendors can prioritize and analyze the uncertainty and ambiguousness among these security challenges.

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Section: Literature Reviewmentioning

confidence: 99%

Identification and prioritization of security challenges of big data on cloud computing based on SLR: A fuzzy‐TOPSIS analysis approach

Khan

et al. 2021

J Software Evolu Process

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“…Again, there is no existing real life dataset corresponding to the information needs of multi-stage tasks of different users. Ideally we should install desktop activity tracking application on thousands of personal desktops, and collect the desktop activity logs of the users of these machines for a period of months and store this data using privacy preserving protocol (Tripathy and Pradhan, 2012;Pise and Uke, 2016). Developing an effective tracking application and deploying it on thousands of desktops would take considerable resource and an enormous amount of infrastructural support.…”

Section: Reference Set Computation For Multi-stage Taskmentioning

confidence: 99%

Proactive information retrieval

Sen

2021

SIGIR Forum

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Users interact with digital systems with some underlying tasks in their minds. In our research scope, a task can be either single or multi-staged. A single-staged task is associated with a single information need, whereas a multi-staged one is associated with more than one information needs. An example of a single-staged task is suggesting related papers to an author while they are writing a section of the research papers. An example of a multi-staged task is planning for a vacation, where the different underlying information needs could be 'places to visit', 'booking accommodation' etc. In the process of accomplishing their task objectives, a user often needs to interact with an information retrieval (IR) system to address one or more information needs. For instance, for writing a research paper on a chosen topic, a user needs to look for existing research work related to the topic. Traditional IR systems do not take into account a user's task intent while showing search results to the user. In our work, we propose a methodology towards developing next generation IR systems (i.e. proactive IR systems) which seek to anticipate the task intent of a user from their interactions with digital systems in order to proactively suggest potentially relevant information sources to assist them to complete their tasks. Specifically speaking, in this PhD, we proposed an embedding approach that captures the task semantics from the interactions of a user with a digital system (e.g. laptop, desktop, smartphone etc.). The proposed embedding approach is then applied for the downstream tasks of providing proactive suggestions in both single and multi staged scenarios. For the single-staged task, we propose a simulation setup to simulate a user's reading and writing interactions in a desktop environment. For the multi-staged task, we focus on web search sessions where a user can have multiple information needs corresponding to a search task. We also proposed a reproducible evaluation framework to compare between different proactive suggestion models. Awarded by : Dublin City University, Ireland on 10 August 2021. Supervised by : Gareth Jones. Available at : https://procheta.github.io/sprocheta/Thesis.pdf.

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“…Koe and Lin 29 proposed a proxy reencryption scheme based on offline type, which is convenient to hide the identity of the data owner and data user from the cloud, and to ensure user identity privacy and category privacy through blind decryption. Pise and Uke 30 proposed an efficient security framework for sensitive data sharing and privacy protection on big data and cloud platforms, using a proxy reencryption method to implement virtual machine monitor‐based security and privacy communications makes sensitive information securely shared on big data platforms. Baboolal et al 31 proposed to use proxy reencryption technology to protect the privacy of drone videos, and used a different key to encrypt the encrypted video data a second time to prevent third parties from obtaining the key to access the video accidentally.…”

Section: Related Researchesmentioning

confidence: 99%

A data privacy protection scheme for Internet of things based on blockchain

Gong

Mei

Feng³

et al. 2020

Trans Emerging Tel Tech

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In recent years, the Internet of things (IoT) equipment has grown rapidly, and the scale of the IoT has also expanded. The IoT is deployed in the system in a centralized manner. At the same time that massive data has put a certain amount of pressure on the storage, the open network environment has not fully protected the privacy of the IoT data, which has become one of the important factors restricting the development of the IoT. Blockchain is a point‐to‐point distributed ledger technology based on cryptographic algorithms. The characteristics of decentralization, tamper resistance, anonymity, and public verifiability can alleviate data security issues in IoT. Ring signature and proxy reencryption are common encryption technologies in the field of privacy protection. Therefore, this article combines blockchain technology with ring signature and proxy reencryption to propose a privacy protection solution for the IoT. Through this solution, the data authorized for sharing in the IoT is transmitted in the system in the form of ciphertext, the identity information of the data sender is protected, and the distributed ledger eases the pressure of mass data storage on a centralized server. The correctness and safety of the proposed scheme are also analyzed.

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Efficient Security Framework for Sensitive Data Sharing and Privacy Preserving on Big-Data and Cloud Platforms

Cited by 7 publications

References 5 publications

Identification and prioritization of security challenges of big data on cloud computing based on SLR: A fuzzy‐TOPSIS analysis approach

Identification and prioritization of security challenges of big data on cloud computing based on SLR: A fuzzy‐TOPSIS analysis approach

Proactive information retrieval

A data privacy protection scheme for Internet of things based on blockchain

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