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Top 100 Branding Trends in February | Trend Hunter, 15 feb 2026
AI and media relations with a bot | BusinessMirror, 15 feb 2026
Why ads are coming to your AI chatbot | The Financial Times, 14 feb 2026
Why marketing leaders are ditching polished headshots for AI caricatures | Marketing-Interactive, 13 feb 2026
Before You Automate Marketing With AI, Decide What Should Never Be Automated | Forbes, 13 feb 2026
Ethical Marketing Despite Algorithmic Bias: The CEO's Responsibility | Forbes, 13 feb 2026
Breaking free from data prison with a roadmap to unified customer insights | MarTech, 11 feb 2026
The cultural forces shaping tomorrow's consumer | National Retail Federation, 10 feb 2026
The customer relationship model: The modern alternative to the brand funnel | AdNews, 09 feb 2026
The 43 best marketing resources we recommend in 2026 | Sprout Social, 07 feb 2026
Can Customers Find Your Brand? Marketing Strategies for AI-Driven Search | MIT Sloan Management Review, 01 feb 2026
How New-Age Social Media Marketing Is Changing and What You Need to Know in 2026 | Business.com, 01 feb 2026
January 2024
Mohammad Anas Wahaj | 11 jan 2024
According to the research 'Reidentification Risk in Panel Data: Protecting for k-Anonymity' (Authors: Sachin Gupta of Cornell University; Shaobo Li of University of Kansas; Matthew J. Schneider of Drexel University; Yan Yu of University of Cincinnati), published on 07 oct 2022 in Information Systems Research, nearly all market research panel participants are at risk of becoming de-anonymized. The commitment of a market research company towards privacy of panelists cannot be totally practiced as there are ways around it. Prof. Sachin Gupta says, 'When organizations release or share data, they are complying with privacy regulations, which means that they’re suppressing or anonymizing personally identifiable information. And they think that they have now protected the privacy of the individuals that they’re sharing the data about. But that, in fact, may not be true, because data can always be linked with other data.' Earlier research (2006) 'How To Break Anonymity of the Netflix Prize Dataset' (Authors: Arvind Narayanan of Princeton University; Vitaly Shmatikov of Cornell University) showcases the similar risk. Researchers developed a de-anonymization algorithm, Scoreboard-RH, that was able to identify up to 99% of Netflix subscribers by using anonymized information from a 2006 competition, aimed at improving its recommendation service, coupled with publicly available info on Internet Movie Database. Both of these researchs rely on 'quasi-identifiers' or QIDs, which are attributes that are common in both an anonymized dataset and a publicly available dataset, which can be used to link them. The conventional measure of disclosure risk, termed unicity, is the proportion of individuals with unique QIDs in a given dataset; k-anonymity is a popular data privacy model aimed to protect against disclosure risk by reducing the degree of uniqueness of QIDs. Prof. Gupta suggests that even though privacy laws are getting tougher but market researchers will continue to collect and store data, and the challenge of privacy remains. He says, 'The nature of the problem will probably reduce and change, but I don't think it's going away. Read on...
Cornell Chronicle:
Protecting identities of panelists in market research
Author:
Tom Fleischman
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