Results 81 to 90 of about 524,437 (218)
Machine Learning with Membership Privacy using Adversarial Regularization [PDF]
Conference on Computer and Communications Security, 2018 Machine learning models leak significant amount of information about their training sets, through their predictions. This is a serious privacy concern for the users of machine learning as a service.
Milad Nasr, R. Shokri, Amir Houmansadr
semanticscholar +1 more source
CybersecurityFully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve ...
Yanwei Gong +5 more
doaj +1 more source
Fog Service in Space Information Network: Architecture, Use Case, Security and Challenges
IEEE Access, 2020 As a large heterogeneous information infrastructure, space terrestrial coinformation network provides a reliable and effective services for all types of space-based users, aviation users, marine users and land-based users through satellite networks ...
Junyan Guo, Ye Du
doaj +1 more source
DATA SUBJECT ACCESS REQUEST: WHAT INDONESIA CAN LEARN AND OPERATIONALISE IN 2024?
Journal of Central Banking Law and Institutions, 2023 The enactment of the Indonesian Personal Data Protection (PDP) Law is in line with the nation’s position as the most promising digital economy in Southeast Asia.
Muhammad Deckri Algamar +1 more
doaj +1 more source
Technical Privacy Metrics: a Systematic Survey [PDF]
Isabel Wagner and David Eckhoff. 2018. Technical Privacy Metrics:
a Systematic Survey. ACM Comput. Surv. 51, 3, Article 57 (June 2018), 2015 The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature makes an informed choice of metrics
arxiv +1 more source
Differential Privacy By Sampling [PDF]
arXiv, 2017 In this paper we present the Sampling Privacy mechanism for privately releasing personal data. Sampling Privacy is a sampling based privacy mechanism that satisfies differential privacy.
arxiv
ethp2psim: Evaluating and deploying privacy-enhanced peer-to-peer routing protocols for the Ethereum network [PDF]
arXiv, 2023 Network-level privacy is the Achilles heel of financial privacy in cryptocurrencies. Financial privacy amounts to achieving and maintaining blockchain- and network-level privacy. Blockchain-level privacy recently received substantial attention. Specifically, several privacy-enhancing technologies were proposed and deployed to enhance blockchain-level ...
arxiv
Differential Privacy as a Mutual Information Constraint [PDF]
, 2016 Differential privacy is a precise mathematical constraint meant to ensure privacy of individual pieces of information in a database even while queries are being answered about the aggregate. Intuitively, one must come to terms with what differential privacy does and does not guarantee.
arxiv +1 more source
Understanding How to Inform Blind and Low-Vision Users about Data Privacy through Privacy Question Answering Assistants [PDF]
arXiv, 2023 Understanding and managing data privacy in the digital world can be challenging for sighted users, let alone blind and low-vision (BLV) users. There is limited research on how BLV users, who have special accessibility needs, navigate data privacy, and how potential privacy tools could assist them.
arxiv
Assessing Optimizer Impact on DNN Model Sensitivity to Adversarial Examples
IEEE Access, 2019 Deep Neural Networks (DNNs) have been gaining state-of-the-art achievement compared with many traditional Machine Learning (ML) models in diverse fields. However, adversarial examples challenge the further deployment and application of DNNs. Analysis has
Yixiang Wang +5 more
doaj +1 more source