anasmark
Topic: advertising | analytics & research | authors | branding | b2b | communication | content | customer | digital & technology | general | human resources | mypitch | people | public relations | retail | sales | university research
Date: 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | jan'22 | feb'22 | mar'22 | apr'22 | may'22 | jun'22 | jul'22 | aug'22 | sep'22 | oct'22 | nov'22 | dec'22 | jan'23 | feb'23 | mar'23 | apr'23 | may'23 | jun'23 | jul'23 | aug'23 | sep'23 | oct'23 | nov'23 | dec'23 | jan'24 | feb'24 | mar'24 | apr'24 | may'24 | jun'24 | jul'24 | aug'24 | sep'24 | oct'24 | nov'24 | dec'24
Headlines
Exceeding Expectations: How To Leverage AI In Healthcare Marketing | Forbes, 17 mar 2025
Traditional marketing vs. digital marketing in 2025 | Specialty Fabrics Review, 17 mar 2025
5 email marketing tech essentials for 2025 success | MarTech, 17 mar 2025
Why B2B marketing matters more than ever for growth and revenue | Okoone, 17 mar 2025
Reddit to let users hide ads from specific advertisers | Marketing-Interactive, 17 mar 2025
Shifts in policy and culture mean advertising must be more purposeful than ever | CreativeBrief, 17 mar 2025
Decoding Consumer Behavior Through Payment Data Analytics | Payments Dive, 17 mar 2025
AI will be the future of customer engagement, but it pays to take it slow | Customer Think, 16 mar 2025
Considering A Co-Branding Opportunity? 14 Tips To Avoid Brand Dilution | Forbes, 12 mar 2025
How To Create a Public Relations Strategy in 2025 | DesignRush, 28 jan 2025
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
©2025, ilmeps
disclaimer & privacy