297 research outputs found
Decentralized Matrix Factorization with Heterogeneous Differential Privacy
Conventional matrix factorization relies on centralized collection of users'
data for recommendation, which might introduce an increased risk of privacy
leakage especially when the recommender is untrusted. Existing differentially
private matrix factorization methods either assume the recommender is trusted,
or can only provide a uniform level of privacy protection for all users and
items with untrusted recommender. In this paper, we propose a novel
Heterogeneous Differentially Private Matrix Factorization algorithm (denoted as
HDPMF) for untrusted recommender. To the best of our knowledge, we are the
first to achieve heterogeneous differential privacy for decentralized matrix
factorization in untrusted recommender scenario. Specifically, our framework
uses modified stretching mechanism with an innovative rescaling scheme to
achieve better trade off between privacy and accuracy. Meanwhile, by allocating
privacy budget properly, we can capture homogeneous privacy preference within a
user/item but heterogeneous privacy preference across different users/items.
Theoretical analysis confirms that HDPMF renders rigorous privacy guarantee,
and exhaustive experiments demonstrate its superiority especially in strong
privacy guarantee, high dimension model and sparse dataset scenario.Comment: Accepted by the 22nd IEEE International Conference on Trust, Security
and Privacy in Computing and Communications (TrustCom-2023
FED-CD: Federated Causal Discovery from Interventional and Observational Data
Causal discovery, the inference of causal relations from data, is a core task
of fundamental importance in all scientific domains, and several new machine
learning methods for addressing the causal discovery problem have been proposed
recently. However, existing machine learning methods for causal discovery
typically require that the data used for inference is pooled and available in a
centralized location. In many domains of high practical importance, such as in
healthcare, data is only available at local data-generating entities (e.g.
hospitals in the healthcare context), and cannot be shared across entities due
to, among others, privacy and regulatory reasons. In this work, we address the
problem of inferring causal structure - in the form of a directed acyclic graph
(DAG) - from a distributed data set that contains both observational and
interventional data in a privacy-preserving manner by exchanging updates
instead of samples. To this end, we introduce a new federated framework,
FED-CD, that enables the discovery of global causal structures both when the
set of intervened covariates is the same across decentralized entities, and
when the set of intervened covariates are potentially disjoint. We perform a
comprehensive experimental evaluation on synthetic data that demonstrates that
FED-CD enables effective aggregation of decentralized data for causal discovery
without direct sample sharing, even when the contributing distributed data sets
cover disjoint sets of interventions. Effective methods for causal discovery in
distributed data sets could significantly advance scientific discovery and
knowledge sharing in important settings, for instance, healthcare, in which
sharing of data across local sites is difficult or prohibited
Privacy-preserving recommendation system using federated learning
Federated Learning is a form of distributed learning which leverages edge devices for training. It aims to preserve privacy by communicating users’ learning parameters and gradient updates to the global server during the training while keeping the actual data on the users’ devices. The training on global server is performed on these parameters instead of user data directly while fine tuning of the model can be done on client’s devices locally. However, federated learning is not without its shortcomings and in this thesis, we present an overview of the learning paradigm and propose a new federated recommender system framework that utilizes homomorphic encryption. This results in a slight decrease in accuracy metrics but leads to greatly increased user-privacy. We also show that performing computations on encrypted gradients barely affects the recommendation performance while ensuring a more secure means of communicating user gradients to and from the global server
A survey of secure middleware for the Internet of Things
The rapid growth of small Internet connected devices, known as the Internet of Things (IoT), is creating a new set of challenges to create secure, private infrastructures. This paper reviews the current literature on the challenges and approaches to security and privacy in the Internet of Things, with a strong focus on how these aspects are handled in IoT middleware. We focus on IoT middleware because many systems are built from existing middleware and these inherit the underlying security properties of the middleware framework. The paper is composed of three main sections. Firstly, we propose a matrix of security and privacy threats for IoT. This matrix is used as the basis of a widespread literature review aimed at identifying requirements on IoT platforms and middleware. Secondly, we present a structured literature review of the available middleware and how security is handled in these middleware approaches. We utilise the requirements from the first phase to evaluate. Finally, we draw a set of conclusions and identify further work in this area
Privacy-Preserving Distributed SVD via Federated Power
Singular value decomposition (SVD) is one of the most fundamental tools in
machine learning and statistics.The modern machine learning community usually
assumes that data come from and belong to small-scale device users. The low
communication and computation power of such devices, and the possible privacy
breaches of users' sensitive data make the computation of SVD challenging.
Federated learning (FL) is a paradigm enabling a large number of devices to
jointly learn a model in a communication-efficient way without data sharing. In
the FL framework, we develop a class of algorithms called FedPower for the
computation of partial SVD in the modern setting. Based on the well-known power
method, the local devices alternate between multiple local power iterations and
one global aggregation to improve communication efficiency. In the aggregation,
we propose to weight each local eigenvector matrix with Orthogonal Procrustes
Transformation (OPT). Considering the practical stragglers' effect, the
aggregation can be fully participated or partially participated, where for the
latter we propose two sampling and aggregation schemes. Further, to ensure
strong privacy protection, we add Gaussian noise whenever the communication
happens by adopting the notion of differential privacy (DP). We theoretically
show the convergence bound for FedPower. The resulting bound is interpretable
with each part corresponding to the effect of Gaussian noise, parallelization,
and random sampling of devices, respectively. We also conduct experiments to
demonstrate the merits of FedPower. In particular, the local iterations not
only improve communication efficiency but also reduce the chance of privacy
breaches
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