Security and Privacy for Big Data - Part 2

Start Date: 03/24/2019

Course Type: Common Course

Course Link: https://www.coursera.org/learn/security-privacy-big-data-protection

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About Course

This course sensitizes regarding privacy and data protection in Big Data environments. You will discover privacy preserving methodologies, as well as data protection regulations and concepts in your Big Data system. By the end of the course, you will be ready to plan your next Big Data project successfully, ensuring that all privacy and data protection related issues are under control. You will look at decent-sized big data projects with privacy-skilled eyes, being able to recognize dangers. This will allow you to improve your systems to a grown and sustainable level. If you are an ICT professional or someone who designs and manages systems in big data environments, this course is for you! Knowledge about Big Data and IT is advantageous, but if you are e.g. a product manager just touching the surface of Big Data and privacy, this course will suit you as well.

Course Syllabus

SECURITY AND PRIVACY FOR BIG DATA - PART 2

Deep Learning Specialization on Coursera

Course Introduction

This course sensitizes regarding privacy and data protection in Big Data environments. You will discover privacy preserving methodologies, as well as

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Article Example
Personal Data Privacy and Security Act of 2009 Requires a business entity that is subject to data privacy and security requirements to: (1) implement a comprehensive personal data privacy and security program to ensure the privacy, security, and confidentiality of sensitive personally identifying information and to protect against breaches of and unauthorized access to such information that could create a significant risk of harm or fraud to any individual; (2) conduct risk assessments of potential security breaches; (3) adopt risk management and control policies and procedures; (4) ensure employee training and supervision for implementation of data security programs; and (5) undertake vulnerability testing and monitoring of personal data privacy and security programs.
Data Privacy Day In response to the increasing levels of data breaches and the global importance of privacy and data security, in 2009 the Online Trust Alliance (OTA) and dozens of global organizations embraced Data Privacy Day as Data Privacy & Protection Day, emphasizing the need to look at the long-term impact to consumers of data collection, use and protection practices. Other organizations including the National Cyber Security Alliance work to coordinate Data Privacy Day activities in the U.S.[4]
Personal Data Privacy and Security Act of 2009 The Personal Data Privacy and Security Act of 2009 (Official title: "A bill to prevent and mitigate identity theft, to ensure privacy, to provide notice of security breaches, and to enhance criminal penalties, law enforcement assistance, and other protections against security breaches, fraudulent access, and misuse of personally identifiable information"), was a bill proposed in the United States Congress to increase protection of personally identifiable information by private companies and government agencies, set guidelines and restrictions on personal data sharing by data brokers, and to enhance criminal penalty for identity theft and other violations of data privacy and security.
Big data Research on the effective usage of information and communication technologies for development (also known as ICT4D) suggests that big data technology can make important contributions but also present unique challenges to International development. Advancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas such as health care, employment, economic productivity, crime, security, and natural disaster and resource management. Additionally, user-generated data offers new opportunities to give the unheard a voice. However, longstanding challenges for developing regions such as inadequate technological infrastructure and economic and human resource scarcity exacerbate existing concerns with big data such as privacy, imperfect methodology, and interoperability issues.
Information privacy The challenge of data privacy is to utilize data while protecting individual's privacy preferences and their personally identifiable information. The fields of computer security, data security, and information security design and utilize software, hardware, and human resources to address this issue. Since the laws and regulations related to Privacy and Data Protection are constantly changing, it is important to keep abreast of any changes in the law and to continually reassess compliance with data privacy and security regulations.
Personal Data Privacy and Security Act of 2009 Imposes civil penalties on business entities that violate the data privacy and security requirements of this subtitle. Grants enforcement authority for such requirements to the FTC.
Center for Urban Science and Progress CUSP sponsored and contributed to "Big Data, Privacy, and the Public Good: Frameworks for Engagement", a pioneering book on the intersection of big data, privacy and the public.
Data Privacy Lab Some of the projects currently underway in the Data Privacy Lab at Harvard School are related to re-identification, discrimination in online ads, privacy-enhanced linking, fingerprint capture, genomic privacy and complex-care patients. The Data Privacy Lab at The University of North Carolina at Charlotte conducts research in various areas like privacy preserving data mining, privacy issues in social networks, privacy aware database generation for software testing and privacy and anonymity in data integration and dissemination.
Data Privacy Day A few of the participating organizations for the January 28, 2016 Data Privacy and Protection day include; Anti-Phishing Working Group, California Office of Privacy Protection, Carnegie Mellon University, Cyber Data-Risk Managers, EDUCAUSE, Georgetown University, Federal Trade Commission (FTC), Federal Communication Commission (FCC), Federal Bureau of Investigation (FBI), Identity Theft Council, the Privacy Commissioner of Canada, New York State Attorney General Office, [https://otalliance.org Online Trust Alliance] (OTA), the UK Information Commissioner., and Data Security Council of India.
Privacy engineering Towards the more implementation levels, privacy engineering employs privacy enhancing technologies to enable anonymisation and de-identification of data. Privacy engineering requires suitable security engineering practices to be deployed, and some privacy aspects can be implemented using security techniques. A privacy impact assessment is another tool within this context and its use does not imply that privacy engineering is being practiced.
Chief Privacy Officer, Department of Homeland Security Pursuant to Section 222 of the Homeland Security Act of 2002, The Chief Privacy Officer has primary responsibility for privacy policy at the Department of Homeland Security, to include: assuring that the technologies used by the Department to protect the United States sustain, and do not erode, privacy protections relating to the use, collection, and disclosure of personal information; assuring that the Department complies with fair information practices as set out in the Privacy Act of 1974; conducting privacy impact assessments of proposed rules at the Department; evaluating legislative and regulatory proposals involving collection, use, and disclosure of personal information by the federal government; and preparing an annual report to Congress on the activities of the Department that affect privacy. The Chief Privacy Officer oversees the Privacy Office, an agency staffed by over fifty privacy and data security professionals including a Deputy Chief Privacy Officer, a Chief Counsel, and advisors who maintain liaison with the DHS Data Privacy and Integrity Advisory Committee.
Big data In the provocative article "Critical Questions for Big Data", the authors title big data a part of mythology: "large data sets offer a higher form of intelligence and knowledge [...], with the aura of truth, objectivity, and accuracy". Users of big data are often "lost in the sheer volume of numbers", and "working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth". Recent developments in BI domain, such as pro-active reporting especially target improvements in usability of big data, through automated filtering of non-useful data and correlations.
Big data Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."
Big data Encrypted search and cluster formation in big data was demonstrated in March 2014 at the American Society of Engineering Education. Gautam Siwach engaged at "Tackling the challenges of Big Data" by MIT Computer Science and Artificial Intelligence Laboratory and Dr. Amir Esmailpour at UNH Research Group investigated the key features of big data as formation of clusters and their interconnections. They focused on the security of big data and the actual orientation of the term towards the presence of different type of data in an encrypted form at cloud interface by providing the raw definitions and real time examples within the technology. Moreover, they proposed an approach for identifying the encoding technique to advance towards an expedited search over encrypted text leading to the security enhancements in big data.
Big data Furthermore, big data analytics results are only as good as the model on which they are predicated. In an example, big data took part in attempting to predict the results of the 2016 U.S. Presidential Election with varying degrees of success. Forbes predicted "If you believe in "Big Data" analytics, it’s time to begin planning for a Hillary Clinton presidency and all that entails.".
Data-centric security Data-centric security is an approach to security that emphasizes the security of the data itself rather than the security of networks, servers, or applications. Data-centric security is evolving rapidly as enterprises increasingly rely on digital information to run their business and big data projects become mainstream.
Personal Data Privacy and Security Act of 2009 Requires the Administrator of the General Services Administration (GSA), in awarding contracts totaling more than $500,000 to data brokers, to evaluate their data privacy and security programs, their compliance, the extent to which their databases and systems have been compromised by security.
Data Privacy Day Data Privacy Day (known in Europe as Data Protection Day) is an international holiday that occurs every 28 January. The purpose of Data Privacy Day is to raise awareness and promote privacy and data protection best practices. It is currently observed in the United States, Canada, India and 47 European countries.
NHS Connecting for Health NPfIT has been criticised for inadequate attention to security and patient privacy, with the Public Accounts Committee noting "patients and doctors have understandable concerns about data security", and that the Department of Health did not have a full picture of data security across the NHS. In 2000, the NHS Executive won the "Most Heinous Government Organisation" Big Brother Award from Privacy International for its plans to implement what would become the NPfIT. In 2004 the NPfIT won the "Most Appalling Project" Big Brother Award "because of its plans to computerise patient records without putting in place adequate privacy safeguards".
Big data Big data analysis is often shallow compared to analysis of smaller data sets. In many big data projects, there is no large data analysis happening, but the challenge is the extract, transform, load part of data preprocessing.