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Headlines
AI is taking ad targeting to a new level. Here's how | QUARTZ, 12 nov 2024
The Problem With Travel Brand Marketing - And How To Fix It | Skift, 12 nov 2024
Performance vs. branding? You're asking the wrong question | Campaign Asia, 12 nov 2024
PR IN HEALTHTECH: BREAKING DOWN COMPLEX INNOVATIONS FOR THE PUBLIC | Medical Research, 12 nov 2024
Connected Packaging Analytics: Understanding Consumer Behaviour Through Data | Little Black Book, 12 nov 2024
The Power of Journey Mapping: Enhancing Customer Experience | USDA, 12 nov 2024
CPMs Are The Wrong Metrics To Prioritize In CTV Advertising | AdExchanger, 11 nov 2024
26 Predictions for Social Media Marketing in 2025 | SocialMediaToday, 10 nov 2024
Enhancing Customer Experience With AI: A Guide to Implementing Conversational Analytics | TechBullion, 07 nov 2024
New study shows inclusive advertising is better for business | Unilever, 28 sep 2024
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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