120 research outputs found
A system-theoretic framework for privacy preservation in continuous-time multiagent dynamics
In multiagent dynamical systems, privacy protection corresponds to avoid
disclosing the initial states of the agents while accomplishing a distributed
task. The system-theoretic framework described in this paper for this scope,
denoted dynamical privacy, relies on introducing output maps which act as
masks, rendering the internal states of an agent indiscernible by the other
agents as well as by external agents monitoring all communications. Our output
masks are local (i.e., decided independently by each agent), time-varying
functions asymptotically converging to the true states. The resulting masked
system is also time-varying, and has the original unmasked system as its limit
system. When the unmasked system has a globally exponentially stable
equilibrium point, it is shown in the paper that the masked system has the same
point as a global attractor. It is also shown that existence of equilibrium
points in the masked system is not compatible with dynamical privacy.
Application of dynamical privacy to popular examples of multiagent dynamics,
such as models of social opinions, average consensus and synchronization, is
investigated in detail.Comment: 38 pages, 4 figures, extended version of arXiv preprint
arXiv:1808.0808
Privacy-Preserving Distributed Optimization and Learning
Distributed optimization and learning has recently garnered great attention
due to its wide applications in sensor networks, smart grids, machine learning,
and so forth. Despite rapid development, existing distributed optimization and
learning algorithms require each agent to exchange messages with its neighbors,
which may expose sensitive information and raise significant privacy concerns.
In this survey paper, we overview privacy-preserving distributed optimization
and learning methods. We first discuss cryptography, differential privacy, and
other techniques that can be used for privacy preservation and indicate their
pros and cons for privacy protection in distributed optimization and learning.
We believe that among these approaches, differential privacy is most promising
due to its low computational and communication complexities, which are
extremely appealing for modern learning based applications with high dimensions
of optimization variables. We then introduce several differential-privacy
algorithms that can simultaneously ensure privacy and optimization accuracy.
Moreover, we provide example applications in several machine learning problems
to confirm the real-world effectiveness of these algorithms. Finally, we
highlight some challenges in this research domain and discuss future
directions.Comment: Accepted as a chapter in the Encyclopedia of Systems and Control
Engineering published by Elsevie
CURRENT TRENDS AND CHALLENGES IN DISTRIBUTED CONTROL SYSTEMS – AN OVERVIEW
In this paper, innovations in the field of distributed control systems have been considered. Without any claim for completeness, a short summary on current trends in this area has been provided. A special attention is paid to application of blockchain technologies in distributed control systems, game theoretical approach for distributed control applications, and advantages of distributed control for power systems. Also, one of the main issues of modern distributed control systems – cybersecurity has been considered
Privacy Analysis of Affine Transformations in Cloud-based MPC: Vulnerability to Side-knowledge
Search for the optimizer in computationally demanding model predictive
control (MPC) setups can be facilitated by Cloud as a service provider in
cyber-physical systems. This advantage introduces the risk that Cloud can
obtain unauthorized access to the privacy-sensitive parameters of the system
and cost function. To solve this issue, i.e., preventing Cloud from accessing
the parameters while benefiting from Cloud computation, random affine
transformations provide an exact yet light weight in computation solution. This
research deals with analyzing privacy preserving properties of these
transformations when they are adopted for MPC problems. We consider two common
strategies for outsourcing the optimization required in MPC problems, namely
separate and dense forms, and establish that random affine transformations
utilized in these forms are vulnerable to side-knowledge from Cloud.
Specifically, we prove that the privacy guarantees of these methods and their
extensions for separate form are undermined when a mild side-knowledge about
the problem in terms of structure of MPC cost function is available. In
addition, while we prove that outsourcing the MPC problem in the dense form
inherently leads to some degree of privacy for the system and cost function
parameters, we also establish that affine transformations applied to this form
are nevertheless prone to be undermined by a Cloud with mild side-knowledge.
Numerical simulations confirm our results
Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes of real-time data streams. Designing accurate models using such data streams, to revolutionize the decision-taking process, inaugurates pervasive computing as a worthy paradigm for a better quality-of-life (e.g., smart homes and self-driving cars.). The confluence of pervasive computing and artificial intelligence, namely Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy and latency requirements. In this context, an intelligent resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g., edge nodes and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and reinforcement learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed training and inference across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges
A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions
peer reviewedThe rapid advances in the Internet of Things (IoT) have promoted a revolution
in communication technology and offered various customer services. Artificial
intelligence (AI) techniques have been exploited to facilitate IoT operations
and maximize their potential in modern application scenarios. In particular,
the convergence of IoT and AI has led to a new networking paradigm called
Intelligent IoT (IIoT), which has the potential to significantly transform
businesses and industrial domains. This paper presents a comprehensive survey
of IIoT by investigating its significant applications in mobile networks, as
well as its associated security and privacy issues. Specifically, we explore
and discuss the roles of IIoT in a wide range of key application domains, from
smart healthcare and smart cities to smart transportation and smart industries.
Through such extensive discussions, we investigate important security issues in
IIoT networks, where network attacks, confidentiality, integrity, and intrusion
are analyzed, along with a discussion of potential countermeasures. Privacy
issues in IIoT networks were also surveyed and discussed, including data,
location, and model privacy leakage. Finally, we outline several key challenges
and highlight potential research directions in this important area
- …