Comparative Analysis of Privacy Preserving Approaches for Collaborative Data Processing

2019 ◽  
pp. 199-206 ◽  
Author(s):  
Urvashi Solanki ◽  
Bintu Kadhiwala
Keyword(s):  
Comparative Analysis ◽  
Data Processing ◽  
Privacy Preserving

scholarly journals PDS2: A user-centered decentralized marketplace for privacy preserving data processing

2021 ◽  
Author(s):  
Lodovico Giaretta ◽  
Ioannis Savvidis ◽  
Thomas Marchioro ◽  
Sarunas Girdzijauskas ◽  
George Pallis ◽  
...  
Keyword(s):  
Data Processing ◽  
Privacy Preserving ◽  
User Centered

scholarly journals Data Protection Impact Assessments as rule of law governance mechanisms

Data & Policy ◽  
2020 ◽  
Vol 2 ◽  
Author(s):  
Swee Leng Harris
Keyword(s):  
Human Rights ◽  
Comparative Analysis ◽  
Data Processing ◽  
Case Studies ◽  
Rule Of Law ◽  
Data Protection ◽  
Environmental Law ◽  
Administrative Law ◽  
Automated Data Processing ◽  
Just Society

Abstract Rule of law principles are essential for a fair and just society and apply to government activities regardless of whether those activities are undertaken by a human or automated data processing. This article explores how Data Protection Impact Assessments (DPIAs) could provide a mechanism for improved rule of law governance of data processing systems developed and used by government for public purposes in civil and administrative areas. Applying rule of law principles to two case studies provides a sketch of the issues and concerns that this article’s proposals for DPIAs seek to address. The article undertakes comparative analysis to find relevant principles and concepts for governance of data processing systems, looking at human rights impact assessments, administrative law, and process rights in environmental law. Drawing on this comparative analysis to identify specific recommendations for DPIAs, the article offers guidance on how DPIAs could be used to strengthen the governance of data processing by government in rule of law terms.

A Study on the Performance and Scalability of Apache Flink Over Hadoop MapReduce

2019 ◽  
Vol 2 (1) ◽  
pp. 61-73
Author(s):  
Pankaj Lathar ◽  
K. G. Srinivasa
Keyword(s):  
Comparative Analysis ◽  
Parallel Processing ◽  
Data Processing ◽  
Real World ◽  
Memory Management ◽  
Programming Model ◽  
Raw Data ◽  
Hadoop Mapreduce ◽  
Active Memory ◽  
Real World Problems

With the advancements in science and technology, data is being generated at a staggering rate. The raw data generated is generally of high value and may conceal important information with the potential to solve several real-world problems. In order to extract this information, the raw data available must be processed and analysed efficiently. It has however been observed, that such raw data is generated at a rate faster than it can be processed by traditional methods. This has led to the emergence of the popular parallel processing programming model – MapReduce. In this study, the authors perform a comparative analysis of two popular data processing engines – Apache Flink and Hadoop MapReduce. The analysis is based on the parameters of scalability, reliability and efficiency. The results reveal that Flink unambiguously outperformance Hadoop's MapReduce. Flink's edge over MapReduce can be attributed to following features – Active Memory Management, Dataflow Pipelining and an Inline Optimizer. It can be concluded that as the complexity and magnitude of real time raw data is continuously increasing, it is essential to explore newer platforms that are adequately and efficiently capable of processing such data.

scholarly journals Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 372
Author(s):  
Shwetank Dattatraya Mamdiwar ◽  
Akshith R ◽  
Zainab Shakruwala ◽  
Utkarsh Chadha ◽  
Kathiravan Srinivasan ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Data Processing ◽  
Essential Role ◽  
Communication Technologies ◽  
Wearable Technology ◽  
Healthcare Industry ◽  
Sensor Systems ◽  
Wearable Sensor ◽  
The World ◽  
Healthcare Monitoring

IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to integrate it with IoT-assisted wearable sensor systems seamlessly. This review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms. It compiles various communication technologies and the devices commonly used in IoT-assisted wearable sensor systems and deals with its various applications in healthcare and their advantages to the world. A comparative analysis of all the wearable technology in healthcare is also discussed with tabulation of various research and technology. This review also analyses all the problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare and describes the various future implementations that can be made to the architecture and the technology to improve the healthcare industry.

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