IoT Use Cases in Healthcare and Tourism

Balandina, Ekaterina; Balandin, Sergey; Koucheryavy, Yevgeni; Mouromtsev, Dmitry

doi:10.1109/cbi.2015.16

Cited by 82 publications

(31 citation statements)

References 6 publications

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“…For performance comparison with SEAL, we selected GP and RP when using static datasets, while DC was used with data streams. The main reason for selecting GP, RP, and DC is that they are multidimensional perturbation mechanisms that correlate with the technique used in the linear system of SEAL as given in Equation C. 34. Figure 4 shows the analytical setup which was used to test the performance of SEAL.…”

Section: Resultsmentioning

confidence: 99%

An efficient and scalable privacy preserving algorithm for big data and data streams

Chamikara

Bertók

Liu

et al. 2019

Computers & Security

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The published article can be found at https://doi.A vast amount of valuable data is produced and is becoming available for analysis as a result of advancements in smart cyber-physical systems. The data comes from various sources, such as healthcare, smart homes, smart vehicles, and often includes private, potentially sensitive information that needs appropriate sanitization before being released for analysis. The incremental and fast nature of data generation in these systems necessitates scalable privacy-preserving mechanisms with high privacy and utility. However, privacy preservation often comes at the expense of data utility. We propose a new data perturbation algorithm, SEAL (Secure and Efficient data perturbation Algorithm utilizing Local differential privacy), based on Chebyshev interpolation and Laplacian noise, which provides a good balance between privacy and utility with high efficiency and scalability. Empirical comparisons with existing privacy-preserving algorithms show that SEAL excels in execution speed, scalability, accuracy, and attack resistance. SEAL provides flexibility in choosing the best possible privacy parameters, such as the amount of added noise, which can be tailored to the domain and dataset. few years. These systems often interact with the environment to collect data mainly for analysis, e.g. to allow life activities to be more intelligent, efficient, and reliable [1]. Such data often includes sensitive details, but sharing confidential information with third parties can lead to a privacy breach.From our perspective, privacy can be considered as "Controlled Information Release" [2]. We can define a privacy breach as the release of private/confidential information to an untrusted environment.However, sharing the data with external parties may be necessary for data analysis, such as data mining and machine learning. Smart cyber-physical systems must have the ability to share information while limiting the disclosure of private information to third parties. Privacy-preserving data sharing and privacy-preserving data mining face significant challenges because of the size of the data and the speed at which data are produced. Robust, scalable, and efficient solutions are needed to preserve the privacy of big data and data streams generated by SCPS [3,4]. Various solutions for privacy-preserving data mining (PPDM) have been proposed for data sanitization; they aim to ensure confidentiality and privacy of data during data mining [5,6,7,8].The two main approaches of PPDM are data perturbation [9, 10] and encryption [11,12]. Although encryption provides a strong notion of security, due to its high computation complexity [13] it can be impractical for PPDM of SCPS-generated big data and data streams. Data perturbation, on the other hand, applies certain modifications such as randomization and noise addition to the original data to preserve privacy [14]. These modification techniques are less complex than cryptographic mechanisms [15]. Data perturbation mechanisms such as noise addition [16] an...

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Section: Resultsmentioning

confidence: 99%

An efficient and scalable privacy preserving algorithm for big data and data streams

Chamikara

Bertók

Liu

et al. 2019

Computers & Security

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“…In addition, the developer of an IoT network has also to consider the varying physical infrastructure. For instance, a patient may be riding in a car or train operating in a tunnel or in remote areas without cellular network access; see Balandina et al [12]. Therefore, bandwidth and throughput of such networks need to be carefully utilized without draining the scarce battery resources of a device by too many transmissions.…”

Section: Iot Securitymentioning

confidence: 99%

Recent Advancements in Intrusion Detection Systems for the Internet of Things

Khan

Herrmann

2019

Security and Communication Networks

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Many Internet of Things (IoT) systems run on tiny connected devices that have to deal with severe processor and energy restrictions. Often, the limited processing resources do not allow the use of standard security mechanisms on the nodes, making IoT applications quite vulnerable to different types of attacks. This holds particularly for intrusion detection systems (IDS) that are usually too resource-heavy to be handled by small IoT devices. Thus, many IoT systems are not sufficiently protected against typical network attacks like Denial-of-Service (DoS) and routing attacks. On the other side, IDSs have already been successfully used in adjacent network types like Mobile Ad hoc Networks (MANET), Wireless Sensor Networks (WSN), and Cyber-Physical Systems (CPS) which, in part, face limitations similar to those of IoT applications. Moreover, there is research work ongoing that promises IDSs that may better fit to the limitations of IoT devices. In this article, we will give an overview about IDSs suited for IoT networks. Besides looking on approaches developed particularly for IoT, we introduce also work for the three similar network types mentioned above and discuss if they are also suitable for IoT systems. In addition, we present some suggestions for future research work that could be useful to make IoT networks more secure.

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“…Health-related applications [38], such as LBS serving to increase the quality of life of elderly and disabled when mobility patterns are used for prevention of diseases or predicting the health status [10], fall detection methods [34], estimating the infectious disease dynamics according to human travel patterns [32], or for measuring the exposure to toxical factors [9]; 2)…”

Section: )mentioning

confidence: 99%

A survey of people movement analytics studies in the context of smart cities

Lohan

Kauppinen

Debnath

2016

2016 19th Conference of Open Innovations Association (FRUCT)

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With the advent of the newest emergency call mandates in US and Europe, with the advances in cellular-based and WiFi-based localization solutions, and with the developments of cloud computing and web-based social networks, the location information and movement-related data is becoming easier and easier to collect from the user mobile devices and from the user cloud data and it is more and more used in a variety of Location Based Services and for various network planning and management tasks. The last decade has seen significant research efforts dedicated to analyze the user location and movement data, to extract mobility patterns and features and to use the predicted patterns for a more efficient resource allocation and for better location-based services. In the context of what is called today 'the smart city', user mobility and location data are becoming key components of the smart city architecture and applications. The goal of this paper is to give a compact and comprehensive overview of the challenges and solutions related to collecting, storing, analyzing, visualizing, using or distributing people's movement data and to summarize the purposes of such data in the context of the smart cities and the Internet of Things.

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IoT Use Cases in Healthcare and Tourism

Cited by 82 publications

References 6 publications

An efficient and scalable privacy preserving algorithm for big data and data streams

An efficient and scalable privacy preserving algorithm for big data and data streams

Recent Advancements in Intrusion Detection Systems for the Internet of Things

A survey of people movement analytics studies in the context of smart cities

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