8 research outputs found
Differentially private partitioned variational inference
Learning a privacy-preserving model from sensitive data which are distributed
across multiple devices is an increasingly important problem. The problem is
often formulated in the federated learning context, with the aim of learning a
single global model while keeping the data distributed. Moreover, Bayesian
learning is a popular approach for modelling, since it naturally supports
reliable uncertainty estimates. However, Bayesian learning is generally
intractable even with centralised non-private data and so approximation
techniques such as variational inference are a necessity. Variational inference
has recently been extended to the non-private federated learning setting via
the partitioned variational inference algorithm. For privacy protection, the
current gold standard is called differential privacy. Differential privacy
guarantees privacy in a strong, mathematically clearly defined sense.
In this paper, we present differentially private partitioned variational
inference, the first general framework for learning a variational approximation
to a Bayesian posterior distribution in the federated learning setting while
minimising the number of communication rounds and providing differential
privacy guarantees for data subjects.
We propose three alternative implementations in the general framework, one
based on perturbing local optimisation runs done by individual parties, and two
based on perturbing updates to the global model (one using a version of
federated averaging, the second one adding virtual parties to the protocol),
and compare their properties both theoretically and empirically.Comment: Published in TMLR 04/2023: https://openreview.net/forum?id=55Bcghgic
Novel gradient-based methods for data distribution and privacy in data science
With an increase in the need of storing data at different locations, designing algorithms that can analyze distributed data is becoming more important. In this thesis, we present several gradient-based algorithms, which are customized for data distribution and privacy. First, we propose a provably convergent, second order incremental and inherently parallel algorithm. The proposed algorithm works with distributed data. By using a local quadratic approximation, we achieve to speed-up the convergence with the help of curvature information. We also illustrate that the parallel implementation of our algorithm performs better than a parallel stochastic gradient descent method to solve a large-scale data science problem. This first algorithm solves the problem of using data that resides at different locations. However, this setting is not necessarily enough for data privacy. To guarantee the privacy of the data, we propose differentially private optimization algorithms in the second part of the thesis. The first one among them employs a smoothing approach which is based on using the weighted averages of the history of gradients. This approach helps to decrease the variance of the noise. This reduction in the variance is important for iterative optimization algorithms, since increasing the amount of noise in the algorithm can harm the performance. We also present differentially private version of a recent multistage accelerated algorithm. These extensions use noise related parameter selection and the proposed stepsizes are proportional to the variance of the noisy gradient. The numerical experiments show that our algorithms show a better performance than some well-known differentially private algorithm
Earth Resources, A Continuing Bibliography with Indexes
This bibliography lists 460 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1984. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum