NORC at the University of Chicago mourns the loss of Peter Meyer, a senior fellow in our Health Sciences department and a nationally recognized expert in data confidentiality and security. Peter passed away on August 18, leaving behind a legacy of innovation and dedication in the field of data governance and accessibility. Throughout his career, Peter was driven by a mission to expand access to federal health data for the research community while strengthening systems and processes to protect data privacy. Prior to joining NORC in 2019, Peter served in several leadership roles at the National Center for Health Statistics, including deputy director of the Division of Research Methodology, NCHS Confidentiality Officer, U.S. Public Health Service Commissioned Corps Officer, and the director of the NCHS Research Data Center, where he significantly improved federal data access processes and expanded the number of Research Data Centers across the United States. At NORC, Peter supported the Office of Management and Budget in developing guidelines for privacy and security of federal data related to the Evidence-Based Policymaking Act of 2018. His expertise was instrumental in efforts to balance data accessibility with privacy protection, and he made significant contributions to the development of the Federal Committee on Statistical Methodology’s Data Protection toolkit, a key resource for federal agencies on mechanisms to protect confidential data while maximizing its availability for societal benefit. Peter's contributions to the field won several awards, including the Outstanding Government Service award from the American Public Health Association Statistics Section and multiple NCHS Director's Awards for Excellence in Service. He was also honored with an Excellence in Innovation award for his work in protecting privacy in genetic data analysis and the HHS Secretary’s Award for Distinguished Service for his efforts during the federal response to Hurricane Katrina. Peter also served in the Army for 4 years as an analyst of foreign communications, specializing in Russian. In addition to being an esteemed colleague, Peter was a dear friend to many of us at NORC, the NCHS, as well as many throughout the Federal Statistical System. Our thoughts are with Peter's family and all those whose lives he touched. https://lnkd.in/dsGRU6eb
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Join us for our upcoming (brown bag lunch series) Privacy Bytes session: "Enabling the Continuous PIA through Data Discovery" During this session, we'll discuss practical steps for integrating continuous PIAs with data discovery, and you will be able to engage with peers, share insights, and enhance data privacy strategies! 🔗 Register here:
Enabling the Continuous PIA through Data Discovery
bigmarker.com
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Protecting privacy and ensuring confidentiality in data is a critical component of modernizing our national data infrastructure. The use of blended data presents new considerations for responsible data stewardship. Our new report, Toward a 21st Century National Data Infrastructure: Managing Privacy and Confidentiality Risks with Blended Data, provides a framework for managing disclosure risks that accounts for the unique attributes of blended data and poses a series of questions to guide considered decision-making. Join us for a presentation of the report’s findings and conclusions and for reflections on implications for statistical policy and best practices for managing privacy risk. Register now: https://ow.ly/ciNH50R5apb #Data #DataScience #DataPolicy
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Anonymizing personal data is important for keeping people’s information safe. K-anonymity is a simple and low-cost way to protect privacy while still being able to use the data. By grouping and generalizing data, k-anonymity makes it much harder for anyone to figure out who the data is about. If you’re handling personal data, consider using k-anonymity to help protect it. It’s an easy and effective step you can take to keep your data secure. What Is K-Anonymity? K-anonymity is a technique that makes sure that any person in a dataset looks the same as at least k−1 other people in the dataset. In other words, the information about one person is grouped together with information about other people, making it hard to tell them apart. Why Is K-Anonymity a Good Solution? -Easy to Use: K-anonymity is straightforward. You don’t need to be a computer expert to use it, and it doesn’t require expensive tools or software. -Low Cost: Because it’s simple, k-anonymity is also affordable. You can protect your data without spending a lot of money. -Effective: Even though it’s simple, k-anonymity is very effective. By grouping similar data together, it makes it much harder for someone to figure out who the data belongs to. How Does K-Anonymity Work? Here’s how you can apply k-anonymity to your data: 1-Find the Identifiers: Look for the pieces of information that might reveal someone’s identity, like birthdates, postal codes, or gender. 2-Generalize the Data: Change the information so it’s less specific. For example, instead of using a full birthdate, just use the birth year. 3-Remove Unique Data: If some data is too unique and can’t be grouped easily, you might need to remove or hide it. 4-Always Check the Results: Make sure that each person’s data is grouped with at least k other people’s data so that no one can be easily identified. InfotoIntell
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🚀 Invest in Data Governance🚀 In today’s data-driven world, robust data governance is essential for success. Implementing strong data governance policies for data management, access control, and compliance is crucial. Here’s why investing in data governance matters: 1) Data Security: Ensures your data is protected against breaches and unauthorized access, safeguarding sensitive information. 🔒 2) Privacy: Maintains the privacy of your data, building trust with customers and stakeholders. 🛡️ 3)Regulatory Compliance: Adheres to regulations and standards, avoiding costly fines and reputational damage. 📜 By prioritizing data governance, you can enhance data security, privacy, and regulatory adherence, ensuring your organization’s data integrity and trustworthiness. 🌟 Only 6 weeks until we'll be exhibiting at Big Data LDN ! In the lead-up to the event, we'll be sharing 8 essential Big Data tips. Stay tuned for valuable insights every week. #BigDataLDN #DataGovernance #Compliance #Erisna
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Data masking is important step in protecting, anonymizing sensitive information. #dataanalytics #dataanonymisation #databasemanagement
An In-Depth Guide to Data Masking
testim.io
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Leader, Government Statistics and Data | Director, CNSTAT at the National Academies | Board Secretary (former), Washington Evaluators
Check out our new CNSTAT report! Toward a 21st Century National Data Infrastructure: Managing Privacy and Confidentiality Risks with Blended Data provides a framework for managing disclosure risks that accounts for the unique attributes of blended data and poses a series of questions to guide considered decision-making. Learn more: https://lnkd.in/e6t-yZ96
Toward a 21st Century National Data Infrastructure: Managing Privacy and Confidentiality Risks with Blended Data
nap.nationalacademies.org
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Board Member, Advisor, Speaker, Author Cyber Governance | Resilience | AI | Digital Transformation | Digital Trust | Zero Trust | CMMC | GRC | Complex Program Management | M&A "Where you are the hero of the story"
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Data Protection Leader Magazine | July 2024
dataguidance.com
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What rights do customers have when it comes to their privacy? How can you navigate those rights and uphold their trust and safety? Data privacy is an imperative field to understand as a data-driven professional. Still relevant 3 years on and brought to you by Harvard Business School: Here’s a primer on what data privacy is and four things you need to know. Free e-book included on A Beginner's Guide to Data & Analytics.
Data Privacy: 4 Things Business Professionals Should Know
online.hbs.edu
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