ﻻ يوجد ملخص باللغة العربية
We measure how effective Privacy Enhancing Technologies (PETs) are at protecting users from website fingerprinting. Our measurements use both experimental and observational methods. Experimental methods allow control, precision, and use on new PETs that currently lack a user base. Observational methods enable scale and drawing from the browsers currently in real-world use. By applying experimentally created models of a PETs behavior to an observational data set, our novel hybrid method offers the best of both worlds. We find the Tor Browser Bundle to be the most effective PET amongst the set we tested. We find that some PETs have inconsistent behaviors, which can do more harm than good.
The AN.ON-Next project aims to integrate privacy-enhancing technologies into the internets infrastructure and establish them in the consumer mass market. The technologies in focus include a basis protection at internet service provider level, an im
Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usability. The shortcomings of traditional privacy-enhancing technologies
Transfer learning has become a common solution to address training data scarcity in practice. It trains a specified student model by reusing or fine-tuning early layers of a well-trained teacher model that is usually publicly available. However, besi
Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect sufficient data to build performant recommender systems for a single comp
Blockchains are turning into decentralized computing platforms and are getting worldwide recognition for their unique advantages. There is an emerging trend beyond payments that blockchains could enable a new breed of decentralized applications, and