%0 Book Section %1 series/sci/SchindlerARS06 %A Mahendra, Prateik %B Adaptive and Personalized Semantic Web %D 2015 %E Mahendra, Prateik %I Springer %K Artificial Big Data Data, Federated Intelligence, Learning Privacy, Privacy-Preserving Technologies, %N 1/2 %P 20 %T Harnessing AI for Data Privacy through a Multidimensional Framework %U http://dblp.uni-trier.de/db/series/sci/sci14.html#SchindlerARS06 %V 15 %X Traditional approaches have failed to keep up with privacy requirements as AI and ML systems increasingly interact with vast, sensitive data sets. Currently, most solutions treat privacy as strictly a technical problem or legal checkbox, but rarely both. The presented work this paper uses a multidimensional scheme that serves to reframe data privacy as a fundamental design principle through the integration of four key perspectives: technical structure, regulatory fit, organizational preparedness, and ethical responsibility. We assess such privacy preserving solutions, federated learning, secure multiparty computation, differential privacy, among others, against the real-world constraints such as scalability, latency and compliance. With cross-sector case study and comparative analysis we illustrate that hybrid context-aware deployments can bridge the theory-practice gap. Our contribution is more than just a toolkit: it is a systems-level implementation that allows businesses and organizations to operationalize privacy while embracing innovation. Such work is necessary for future empirical studies to provide a grounded approach to the development of AI systems that are high performing, yet privacy preserving. %@ 2231 - 3117 Online ; 2231 - 3605 Print

@incollection{series/sci/SchindlerARS06, abstract = {Traditional approaches have failed to keep up with privacy requirements as AI and ML systems increasingly interact with vast, sensitive data sets. Currently, most solutions treat privacy as strictly a technical problem or legal checkbox, but rarely both. The presented work this paper uses a multidimensional scheme that serves to reframe data privacy as a fundamental design principle through the integration of four key perspectives: technical structure, regulatory fit, organizational preparedness, and ethical responsibility. We assess such privacy preserving solutions, federated learning, secure multiparty computation, differential privacy, among others, against the real-world constraints such as scalability, latency and compliance. With cross-sector case study and comparative analysis we illustrate that hybrid context-aware deployments can bridge the theory-practice gap. Our contribution is more than just a toolkit: it is a systems-level implementation that allows businesses and organizations to operationalize privacy while embracing innovation. Such work is necessary for future empirical studies to provide a grounded approach to the development of AI systems that are high performing, yet privacy preserving. }, added-at = {2025-06-11T14:56:52.000+0200}, author = {Mahendra, Prateik}, biburl = {https://www.bibsonomy.org/bibtex/2696be539a616a3551e430a17339c043a/ijcseit}, booktitle = {Adaptive and Personalized Semantic Web}, crossref = {series/sci/2006-14}, description = {dblp}, editor = {Mahendra, Prateik}, ee = {http://dx.doi.org/10.1007/3-540-33279-0_7}, interhash = {fcdf7736bf3d0bd38016e534193a9dc5}, intrahash = {696be539a616a3551e430a17339c043a}, isbn = {2231 - 3117 [Online] ; 2231 - 3605 [Print]}, keywords = {Artificial Big Data Data, Federated Intelligence, Learning Privacy, Privacy-Preserving Technologies,}, month = {April}, number = {1/2}, pages = 20, publisher = {Springer}, series = {Studies in Computational Intelligence}, timestamp = {2025-06-11T14:56:52.000+0200}, title = {Harnessing AI for Data Privacy through a Multidimensional Framework}, url = {http://dblp.uni-trier.de/db/series/sci/sci14.html#SchindlerARS06}, volume = 15, year = 2015 }