4,690 research outputs found
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn
Preserving privacy of users is a key requirement of web-scale analytics and
reporting applications, and has witnessed a renewed focus in light of recent
data breaches and new regulations such as GDPR. We focus on the problem of
computing robust, reliable analytics in a privacy-preserving manner, while
satisfying product requirements. We present PriPeARL, a framework for
privacy-preserving analytics and reporting, inspired by differential privacy.
We describe the overall design and architecture, and the key modeling
components, focusing on the unique challenges associated with privacy,
coverage, utility, and consistency. We perform an experimental study in the
context of ads analytics and reporting at LinkedIn, thereby demonstrating the
tradeoffs between privacy and utility needs, and the applicability of
privacy-preserving mechanisms to real-world data. We also highlight the lessons
learned from the production deployment of our system at LinkedIn.Comment: Conference information: ACM International Conference on Information
and Knowledge Management (CIKM 2018
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
The Crypto-democracy and the Trustworthy
In the current architecture of the Internet, there is a strong asymmetry in
terms of power between the entities that gather and process personal data
(e.g., major Internet companies, telecom operators, cloud providers, ...) and
the individuals from which this personal data is issued. In particular,
individuals have no choice but to blindly trust that these entities will
respect their privacy and protect their personal data. In this position paper,
we address this issue by proposing an utopian crypto-democracy model based on
existing scientific achievements from the field of cryptography. More
precisely, our main objective is to show that cryptographic primitives,
including in particular secure multiparty computation, offer a practical
solution to protect privacy while minimizing the trust assumptions. In the
crypto-democracy envisioned, individuals do not have to trust a single physical
entity with their personal data but rather their data is distributed among
several institutions. Together these institutions form a virtual entity called
the Trustworthy that is responsible for the storage of this data but which can
also compute on it (provided first that all the institutions agree on this).
Finally, we also propose a realistic proof-of-concept of the Trustworthy, in
which the roles of institutions are played by universities. This
proof-of-concept would have an important impact in demonstrating the
possibilities offered by the crypto-democracy paradigm.Comment: DPM 201
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