Matches in SemOpenAlex for { <https://semopenalex.org/work/W4286208108> ?p ?o ?g. }
- W4286208108 endingPage "e0271714" @default.
- W4286208108 startingPage "e0271714" @default.
- W4286208108 abstract "The systematic monitoring of private communications through the use of information technology pervades the digital age. One result of this is the potential availability of vast amount of data tracking the characteristics of mobile network users. Such data is becoming increasingly accessible for commercial use, while the accessibility of such data raises questions about the degree to which personal information can be protected. Existing regulations may require the removal of personally-identifiable information (PII) from datasets before they can be processed, but research now suggests that powerful machine learning classification methods are capable of targeting individuals for personalized marketing purposes, even in the absence of PII. This study aims to demonstrate how machine learning methods can be deployed to extract demographic characteristics. Specifically, we investigate whether key demographics-gender and age-of mobile users can be accurately identified by third parties using deep learning techniques based solely on observations of the user's interactions within the network. Using an anonymized dataset from a Latin American country, we show the relative ease by which PII in terms of the age and gender demographics can be inferred; specifically, our neural networks model generates an estimate for gender with an accuracy rate of 67%, outperforming decision tree, random forest, and gradient boosting models by a significant margin. Neural networks achieve an even higher accuracy rate of 78% in predicting the subscriber age. These results suggest the need for a more robust regulatory framework governing the collection of personal data to safeguard users from predatory practices motivated by fraudulent intentions, prejudices, or consumer manipulation. We discuss in particular how advances in machine learning have chiseled away a number of General Data Protection Regulation (GDPR) articles designed to protect consumers from the imminent threat of privacy violations." @default.
- W4286208108 created "2022-07-21" @default.
- W4286208108 creator A5018887021 @default.
- W4286208108 creator A5024154072 @default.
- W4286208108 creator A5034718687 @default.
- W4286208108 creator A5067975955 @default.
- W4286208108 creator A5068724126 @default.
- W4286208108 date "2022-07-21" @default.
- W4286208108 modified "2023-09-30" @default.
- W4286208108 title "Predicting age and gender from network telemetry: Implications for privacy and impact on policy" @default.
- W4286208108 cites W1005075013 @default.
- W4286208108 cites W1488996941 @default.
- W4286208108 cites W164023310 @default.
- W4286208108 cites W1877895303 @default.
- W4286208108 cites W1982300822 @default.
- W4286208108 cites W1993444042 @default.
- W4286208108 cites W2054924919 @default.
- W4286208108 cites W2074338790 @default.
- W4286208108 cites W2099216531 @default.
- W4286208108 cites W2101048693 @default.
- W4286208108 cites W2104851049 @default.
- W4286208108 cites W2128906841 @default.
- W4286208108 cites W2149587241 @default.
- W4286208108 cites W2151554678 @default.
- W4286208108 cites W2203167467 @default.
- W4286208108 cites W2286737780 @default.
- W4286208108 cites W2301141820 @default.
- W4286208108 cites W2348624827 @default.
- W4286208108 cites W2535690855 @default.
- W4286208108 cites W2546093686 @default.
- W4286208108 cites W2563852449 @default.
- W4286208108 cites W2610886376 @default.
- W4286208108 cites W2614507296 @default.
- W4286208108 cites W2735549848 @default.
- W4286208108 cites W2739349903 @default.
- W4286208108 cites W2741445211 @default.
- W4286208108 cites W2772974342 @default.
- W4286208108 cites W2780357604 @default.
- W4286208108 cites W2789323544 @default.
- W4286208108 cites W2795908329 @default.
- W4286208108 cites W2799782400 @default.
- W4286208108 cites W2805153331 @default.
- W4286208108 cites W2890455363 @default.
- W4286208108 cites W2899136066 @default.
- W4286208108 cites W2909807406 @default.
- W4286208108 cites W2911964244 @default.
- W4286208108 cites W2913139997 @default.
- W4286208108 cites W2922295561 @default.
- W4286208108 cites W2922705140 @default.
- W4286208108 cites W2935849356 @default.
- W4286208108 cites W2942272104 @default.
- W4286208108 cites W2945720746 @default.
- W4286208108 cites W2954412990 @default.
- W4286208108 cites W2955594526 @default.
- W4286208108 cites W2957780493 @default.
- W4286208108 cites W2961629612 @default.
- W4286208108 cites W2963275574 @default.
- W4286208108 cites W2963953172 @default.
- W4286208108 cites W2964303497 @default.
- W4286208108 cites W2977027580 @default.
- W4286208108 cites W2980381981 @default.
- W4286208108 cites W2981825257 @default.
- W4286208108 cites W2995364987 @default.
- W4286208108 cites W3002453680 @default.
- W4286208108 cites W3002514201 @default.
- W4286208108 cites W3004542466 @default.
- W4286208108 cites W3010890049 @default.
- W4286208108 cites W3011685663 @default.
- W4286208108 cites W3019166713 @default.
- W4286208108 cites W3024787918 @default.
- W4286208108 cites W3035686316 @default.
- W4286208108 cites W3035693076 @default.
- W4286208108 cites W3038598828 @default.
- W4286208108 cites W3085802240 @default.
- W4286208108 cites W3102476541 @default.
- W4286208108 cites W3112787034 @default.
- W4286208108 cites W3122966722 @default.
- W4286208108 cites W3124321814 @default.
- W4286208108 cites W3125537303 @default.
- W4286208108 cites W3138539755 @default.
- W4286208108 cites W3138657417 @default.
- W4286208108 cites W3176551993 @default.
- W4286208108 cites W4232197496 @default.
- W4286208108 cites W4237591687 @default.
- W4286208108 cites W4239510810 @default.
- W4286208108 cites W4246892532 @default.
- W4286208108 cites W4251140609 @default.
- W4286208108 cites W4288083799 @default.
- W4286208108 cites W4288086175 @default.
- W4286208108 cites W4294969213 @default.
- W4286208108 cites W589244836 @default.
- W4286208108 doi "https://doi.org/10.1371/journal.pone.0271714" @default.
- W4286208108 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35862447" @default.
- W4286208108 hasPublicationYear "2022" @default.
- W4286208108 type Work @default.
- W4286208108 citedByCount "1" @default.
- W4286208108 countsByYear W42862081082023 @default.
- W4286208108 crossrefType "journal-article" @default.