scholarly journals A Comprehensive Survey of Privacy-preserving Federated Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-36
Author(s):  
Xuefei Yin ◽  
Yanming Zhu ◽  
Jiankun Hu
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Research Directions ◽  
Privacy Concerns ◽  
Privacy Leakage ◽  
Comprehensive Survey ◽  
Future Research Directions

The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results. The emerging privacy-preserving FL (PPFL) has been heralded as a solution to generic privacy-preserving machine learning. However, the challenge of protecting data privacy while maintaining the data utility through machine learning still remains. In this article, we present a comprehensive and systematic survey on the PPFL based on our proposed 5W-scenario-based taxonomy. We analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research directions.

Study on distributed privacy preserving data mining

2014 ◽  
Vol 11 (2) ◽  
pp. 163-170
Author(s):  
Binli Wang ◽  
Yanguang Shen
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Distributed Data ◽  
Distributed Environment ◽  
Privacy Preserving Data Mining ◽  
Advantages And Disadvantages ◽  
Future Research Directions

Recently, with the rapid development of network, communications and computer technology, privacy preserving data mining (PPDM) has become an increasingly important research in the field of data mining. In distributed environment, how to protect data privacy while doing data mining jobs from a large number of distributed data is more far-researching. This paper describes current research of PPDM at home and abroad. Then it puts emphasis on classifying the typical uses and algorithms of PPDM in distributed environment, and summarizing their advantages and disadvantages. Furthermore, it points out the future research directions in the field.

A SURVEY OF PRIVACY-PRESERVING COLLABORATIVE FILTERING SCHEMES

2013 ◽  
Vol 23 (08) ◽  
pp. 1085-1108 ◽  
Author(s):  
ALPER BILGE ◽  
CIHAN KALELI ◽  
IBRAHIM YAKUT ◽  
IHSAN GUNES ◽  
HUSEYIN POLAT
Keyword(s):  
Collaborative Filtering ◽  
Privacy Preserving ◽  
Data Partitioning ◽  
Future Research ◽  
Practical Implementation ◽  
Research Directions ◽  
Implementation Challenges ◽  
Confidential Data ◽  
Comprehensive Survey ◽  
Future Research Directions

With increasing need for preserving confidential data while providing recommendations, privacy-preserving collaborative filtering has been receiving increasing attention. To make data owners feel more comfortable while providing predictions, various schemes have been proposed to estimate recommendations without deeply jeopardizing privacy. Such methods eliminate or reduce data owners' privacy, financial, and legal concerns by employing different privacy-preserving techniques. Although there are considerable numbers of studies focusing on privacy-preserving collaborative filtering schemes, there is no comprehensive survey investigating them with respect to different directions. In this survey, we mainly focus on studying various privacy-preserving recommendation methods according to the data partitioning cases and the utilized techniques for preserving confidentiality. We also review privacy in general and examine in collaborative filtering scenarios. We discuss the proposed schemes in terms of their limitations and practical implementation challenges. Moreover, we give an overview of evaluation of such schemes. We finally provide a comprehensive guideline for studying in this area and propose future research directions.

A Survey on Privacy Preserving Dynamic Data Publishing

2018 ◽  
Vol 8 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Salheddine Kabou ◽  
Sidi mohamed Benslimane ◽  
Mhammed Mosteghanemi
Keyword(s):  
Data Privacy ◽  
Privacy Preservation ◽  
Personal Information ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Future Research ◽  
Research Directions ◽  
Dynamic Data ◽  
Practical Applications ◽  
Future Research Directions

Many organizations, especially small and medium business (SMB) enterprises require the collection and sharing of data containing personal information. The privacy of this data must be preserved before outsourcing to the commercial public. Privacy preserving data publishing PPDP refers to the process of publishing useful information while preserving data privacy. A variety of approaches have been proposed to ensure privacy by applying traditional anonymization models which focused only on the single publication of datasets. In practical applications, data publishing is more complicated where the organizations publish multiple times for different recipients or after modifications to provide up-to-date data. Privacy preserving dynamic data publication PPDDP is a new process in privacy preservation which addresses the anonymization of the data for different purposes. In this survey, the author will systematically evaluate and summarize different studies to PPDDP, clarify the differences and requirements between the scenarios that can exist, and propose future research directions.

scholarly journals Privacy-preserving healthcare informatics: a review

2021 ◽  
Vol 36 ◽  
pp. 04005
Author(s):  
Kah Meng Chong
Keyword(s):  
Data Privacy ◽  
Differential Privacy ◽  
State Of The Art ◽  
Future Research ◽  
Sensitive Information ◽  
Research Directions ◽  
Data Anonymization ◽  
Healthcare Data ◽  
Comprehensive Survey ◽  
Future Research Directions

Electronic Health Record (EHR) is the key to an efficient healthcare service delivery system. The publication of healthcare data is highly beneficial to healthcare industries and government institutions to support a variety of medical and census research. However, healthcare data contains sensitive information of patients and the publication of such data could lead to unintended privacy disclosures. In this paper, we present a comprehensive survey of the state-of-the-art privacy-enhancing methods that ensure a secure healthcare data sharing environment. We focus on the recently proposed schemes based on data anonymization and differential privacy approaches in the protection of healthcare data privacy. We highlight the strengths and limitations of the two approaches and discussed some promising future research directions in this area.

MetaLearning with Graph Neural Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Graph Problems ◽  
Meta Learning ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

scholarly journals Carpooling in Connected and Autonomous Vehicles: Current Solutions and Future Directions

2022 ◽  
Author(s):  
Farkhanda Zafar ◽  
Hasan Ali Khattak ◽  
Moayad Aloqaily ◽  
Rasheed Hussain
Keyword(s):  
Business Model ◽  
Autonomous Vehicles ◽  
Research Work ◽  
Relevant Information ◽  
Security And Privacy ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Survey ◽  
One Stop ◽  
Future Research Directions

Owing to the advancements in communication and computation technologies, the dream of commercialized connected and autonomous cars is becoming a reality. However, among other challenges such as environmental pollution, cost, maintenance, security, and privacy, the ownership of vehicles (especially for Autonomous Vehicles (AV)) is the major obstacle in the realization of this technology at the commercial level. Furthermore, the business model of pay-as-you-go type services further attracts the consumer because there is no need for upfront investment. In this vein, the idea of car-sharing ( aka carpooling) is getting ground due to, at least in part, its simplicity, cost-effectiveness, and affordable choice of transportation. Carpooling systems are still in their infancy and face challenges such as scheduling, matching passengers interests, business model, security, privacy, and communication. To date, a plethora of research work has already been done covering different aspects of carpooling services (ranging from applications to communication and technologies); however, there is still a lack of a holistic, comprehensive survey that can be a one-stop-shop for the researchers in this area to, i) find all the relevant information, and ii) identify the future research directions. To fill these research challenges, this paper provides a comprehensive survey on carpooling in autonomous and connected vehicles and covers architecture, components, and solutions, including scheduling, matching, mobility, pricing models of carpooling. We also discuss the current challenges in carpooling and identify future research directions. This survey is aimed to spur further discussion among the research community for the effective realization of carpooling.

Deep Learning and Medical Imaging

2019 ◽  
pp. 101-147
Author(s):  
Nourhan Mohamed Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Future Research ◽  
Research Directions ◽  
Conventional Machine ◽  
Future Research Directions ◽  
Nested Hierarchy ◽  
Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

State of the Art and Key Design Challenges of Telesurgical Robotics

2019 ◽  
pp. 1100-1111
Author(s):  
Sajid Nisar ◽  
Osman Hasan
Keyword(s):  
State Of The Art ◽  
Robotic Systems ◽  
Future Research ◽  
Research Directions ◽  
Remote Locations ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Surgical System ◽  
Surgical Operations ◽  
Design Challenges

Telesurgical robotic systems allow surgeons to perform surgical operations from remote locations with enhanced comfort and dexterity. Introduction of robotic technology has revolutionized operation theaters but its multidisciplinary nature and high associated costs pose significant challenges. This chapter provides a comprehensive survey of the current progress in the field of surgical robotics with a detailed discussion on various state-of-the-art telesurgical robotic systems. The key design approaches and challenges are identified, and their solutions are recommended. A set of parameters that can be used to assess usefulness of a telesurgical robot are discussed. Finally, guidelines for selection of a suitable surgical system and the future research directions are described.

Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals

2021 ◽  
pp. 136-160
Author(s):  
Mercedes Barrachina ◽  
Laura Valenzuela López
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Adult Population ◽  
The United States ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Learning Techniques ◽  
Control And Prevention ◽  
Future Research Directions

Sleep disorders are related to many different diseases, and they could have a significant impact in patients' health, causing an economic impact to the society and to the national health systems. In the United States, according to information from the Center for Disease Control and Prevention, those disorders are affecting 50-70 million in the adult population. Sleep disorders are causing annually around 40,000 deaths due to cardiovascular problems, and they cost the health system more than 16 billion. In other countries, such as in Spain, those disorders affect up to 48% of the adult population. The main objective of this chapter is to review and evaluate the different machine learning techniques utilized by researchers and medical professionals to identify, assess, and characterize sleep disorders. Moreover, some future research directions are proposed considering the evaluated area.

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