Privacy Preservation in Affect-Driven Personalization

Addo, Ivor D.; Madiraju, Praveen; Ahamed, Sheikh Iqbal; Chu, William Cheng‐Chung

doi:10.1109/compsac.2016.168

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Data anonymizing with denaturing framework has been developed with the following aspects: (a) the users have possibility to define rules before the algorithm is deployed, (b) personal data masking system, (c) analytic system to allow denaturing, deletion inference anonymization and mobility data privacy function and a wide range of research has been proposed to satisfy these features [14,15,16,17,18,19].…”

Section: Related Workmentioning

confidence: 99%

An IoT-Based Anonymous Function for Security and Privacy in Healthcare Sensor Networks

Yin

Liu

Bruce³

et al. 2019

Sensors

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In the age of the Internet of Things, connected devices are changing the delivery system in the healthcare communication environment. With the integration of IoT in healthcare, there is a huge potential for improvement of the quality, safety, and efficiency of health care in addition to promising technological, economical, and social prospects. Nevertheless, this integration comes with security risks such as data breach that might be caused by credential-stealing malware. In addition, the patient valuable data can be disclosed when the perspective devices are compromised since they are connected to the internet. Hence, security has become an essential part of today’s computing world regarding the ubiquitous nature of the IoT entities in general and IoT-based healthcare in particular. In this paper, research on the algorithm for anonymizing sensitive information about health data set exchanged in the IoT environment using a wireless communication system has been presented. To preserve the security and privacy, during the data session from the users interacting online, the algorithm defines records that cannot be revealed by providing protection to user’s privacy. Moreover, the proposed algorithm includes a secure encryption process that enables health data anonymity. Furthermore, we have provided an analysis using mathematical functions to valid the algorithm’s anonymity function. The results show that the anonymization algorithm guarantees safety features for the considered IoT system applied in context of the healthcare communication systems.

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Section: Related Workmentioning

confidence: 99%

An IoT-Based Anonymous Function for Security and Privacy in Healthcare Sensor Networks

Yin

Liu

Bruce³

et al. 2019

Sensors

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“…Addo et al [84] present a reference framework for protecting end user's privacy throughout the emotion analytics lifecycle. They propose Affect-Driven Personalization Lifecycle (ADPL), a model to learn the privacy preferences of end users through implementing a set of privacy rules: personalized anonymity, secure multiparty privacy preservation, encrypted data provenance, image-melding and reshaping techniques, and result aggregation.…”

Section: Data Anonymizationmentioning

confidence: 99%

Towards Privacy Preserving IoT Environments: A Survey

Seliem

Elgazzar

Khalil

2018

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is a network of Internet-enabled devices that can sense, communicate, and react to changes in their environment. Billions of these computing devices are connected to the Internet to exchange data between themselves and/or their infrastructure. IoT promises to enable a plethora of smart services in almost every aspect of our daily interactions and improve the overall quality of life. However, with the increasing wide adoption of IoT, come significant privacy concerns to lose control of how our data is collected and shared with others. As such, privacy is a core requirement in any IoT ecosystem and is a major concern that inhibits its widespread user adoption. The ultimate source of user discomfort is the lack of control over personal raw data that is directly streamed from sensors to the outside world. In this survey, we review existing research and proposed solutions to rising privacy concerns from a multipoint of view to identify the risks and mitigations. First, we provide an evaluation of privacy issues and concerns in IoT systems due to resource constraints. Second, we describe the proposed IoT solutions that embrace a variety of privacy concerns such as identification, tracking, monitoring, and profiling. Lastly, we discuss the mechanisms and architectures for protecting IoT data in case of mobility at the device layer, infrastructure/platform layer, and application layer.

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A Study on Security Issues and Attacks, Challenges and Future Improvements in Cloud-based IoT

Praveena¹,

Gunasekhar

2022

SWCC

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: The Internet of Things (IoT) has become a rising and dynamic research area. It is the integration of numerous objects (things) to communicate the information without the intervention of human beings. Inappropriately, because of the qualities resource-constrained, and limited range communication, it significantly relies upon the Cloud as outsourcing to store and compute the data. This reconciliation of IoT with the Cloud has brought new issues and poses difficulties regarding protection and security threats. The necessity for the wide arrangement of cloud-based IoT is rapidly expanding with noteworthy security-related issues. This work plots existing security methodologies and vulnerabilities near to a portion of the present security strategies by a total review of existing work in the field of security in cloud-based IoT. This paper concentrated on security and protection in terms of privacy by investigating some potential difficulties and risks that should be resolved. To accomplish this, the Cloud of Things (CoT) architectures and present applications have been explored and focused on the attacks at the middleware layer. Furthermore, this paper outlines a concise scientific classification of the current security threats in cloud-based IoT, plans, and communication. Finally, a few captivating open issues are given with promising designs to trigger more research attempts in the research community.

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Privacy Preservation in Affect-Driven Personalization

Cited by 3 publications

References 10 publications

An IoT-Based Anonymous Function for Security and Privacy in Healthcare Sensor Networks

An IoT-Based Anonymous Function for Security and Privacy in Healthcare Sensor Networks

Towards Privacy Preserving IoT Environments: A Survey

A Study on Security Issues and Attacks, Challenges and Future Improvements in Cloud-based IoT

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