A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Private and Utility Enhanced Recommendations with Local Differential Privacy and Gaussian Mixture Model
[article]
2021
arXiv
pre-print
Recommendation systems rely heavily on users behavioural and preferential data (e.g. ratings, likes) to produce accurate recommendations. However, users experience privacy concerns due to unethical data aggregation and analytical practices carried out by the Service Providers (SP). Local differential privacy (LDP) based perturbation mechanisms add noise to users data at user side before sending it to the SP. The SP then uses the perturbed data to perform recommendations. Although LDP protects
arXiv:2102.13453v2
fatcat:2272266agjcu3iecib7rtbfjs4