scholarly journals Gait Recognition as an Authentication Method for Mobile Devices

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4110
Author(s):  
Matei-Sorin Axente ◽  
Ciprian Dobre ◽  
Radu-Ioan Ciobanu ◽  
Raluca Purnichescu-Purtan
Keyword(s):  
Data Privacy ◽  
Experimental Testing ◽  
Human Life ◽  
Gait Recognition ◽  
Sampling Rate ◽  
Motion Sensors ◽  
Sensitive Data ◽  
Recognition Mechanism ◽  
Efficient Gait ◽  
Short Time

With the rate at which smartphones are currently evolving, more and more of human life will be contained in these devices. At a time when data privacy is extremely important, it is crucial to protect one’s mobile device. In this paper, we propose a new non-intrusive gait recognition based mechanism that can enhance the security of smartphones by rapidly identifying users with a high degree of confidence and securing sensitive data in case of an attack, with a focus on a potential architecture for such an algorithm for the Android environment. The motion sensors on an Android device are used to create a statistical model of a user’s gait, which is later used for identification. Through experimental testing, we prove the capability of our proposed solution by correctly classifying individuals with an accuracy upwards of 90% when tested on data recorded during multiple activities. The experiments, conducted on a low sampling rate and at short time intervals, show the benefits of our solution and highlight the feasibility of an efficient gait recognition mechanism on modern smartphones.

scholarly journals Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Qi Dou ◽  
Tiffany Y. So ◽  
Meirui Jiang ◽  
Quande Liu ◽  
Varut Vardhanabhuti ◽  
...  
Keyword(s):  
Data Privacy ◽  
Medical Training ◽  
External Validation ◽  
Mainland China ◽  
Privacy Preserving ◽  
Generalization Capability ◽  
Major Focus ◽  
Sensitive Data ◽  
Lung Abnormalities ◽  
Medical Image Diagnosis

AbstractData privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

scholarly journals Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hua Dai ◽  
Hui Ren ◽  
Zhiye Chen ◽  
Geng Yang ◽  
Xun Yi
Keyword(s):  
Data Privacy ◽  
Logistic Map ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Service Recommendation ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Sorting Algorithms ◽  
Common Operation ◽  
Cloud Servers

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

scholarly journals A Secure Ciphertext Retrieval Scheme against Insider KGAs for Mobile Devices in Cloud Storage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Run Xie ◽  
Chanlian He ◽  
Dongqing Xie ◽  
Chongzhi Gao ◽  
Xiaojun Zhang
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Privacy ◽  
Security Analysis ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Security Issues ◽  
Efficiency Comparison ◽  
Cloud Servers

With the advent of cloud computing, data privacy has become one of critical security issues and attracted much attention as more and more mobile devices are relying on the services in cloud. To protect data privacy, users usually encrypt their sensitive data before uploading to cloud servers, which renders the data utilization to be difficult. The ciphertext retrieval is able to realize utilization over encrypted data and searchable public key encryption is an effective way in the construction of encrypted data retrieval. However, the previous related works have not paid much attention to the design of ciphertext retrieval schemes that are secure against inside keyword-guessing attacks (KGAs). In this paper, we first construct a new architecture to resist inside KGAs. Moreover we present an efficient ciphertext retrieval instance with a designated tester (dCRKS) based on the architecture. This instance is secure under the inside KGAs. Finally, security analysis and efficiency comparison show that the proposal is effective for the retrieval of encrypted data in cloud computing.

scholarly journals Results of the infrasonic array on the Valaam Island primary data processing

2020 ◽  
Vol 2 (1) ◽  
pp. 85-91
Author(s):  
Vladimir Karpinsky ◽  
Vladimir Asming
Keyword(s):  
Data Processing ◽  
Seismic Station ◽  
Sampling Rate ◽  
Low Frequency ◽  
Primary Data ◽  
Joint Analysis ◽  
Main Task ◽  
Significant Difference ◽  
Infrasound Signal ◽  
Short Time

The infrasound array VALS developed in Kola Branch GS RAS has been installed in June 2016 on the Valaam Island in addition to the continuously operating seismic station VALR. The array consists of 3 spaced low-frequency microphones. The data with a sampling rate of 100 Hz is stored continuously at the acquisition computer; the timing is carried out using GPS. In addition to the acquisition system, an infrasound signal detector is installed on the computer. It works in near real-time mode and enables us to find signals and compute their back azimuths. At the end of 2018, a new version of the detector was developed at the Kola Branch GS RAS. The detector began to work much faster, which enabled us to carry out data processing for 2.5 years in two frequency ranges in a short time. The main task of the array is acoustic monitoring, the detection of infrasound events, the determination of their parameters, and the selection of events of natural origin. The data are also used (in combination with the VALR seismic station data) to locate near seismic events, especially weak ones. The analysis of the obtained data revealed the prevailing directions to the signal sources. The change of directions to sources in time was investigated, seasonal features were revealed. Acoustic events were detected in the frequency bands 1–5 Hz and 10–20 Hz, and a significant difference was found in the azimuthal distribution of events for these ranges. A joint analysis of acoustic and seismic data showed that the part of events with both acoustic and seismic components is low – it is almost completely exhausted by career explosions. It was also noted that in addition to explosions in nearby quarries (Kuznechnoye, Pitkäranta) located at a distance of 50–60 km, according to acoustic data, events corresponding to explosions at quarries located at a distance of 100 km or more were repeatedly identified.

scholarly journals The Impact of Operating System on Bandwidth in Open VPN Technology

2016 ◽  
Vol 13 (1) ◽  
pp. 204-211
Author(s):  
Baghdad Science Journal
Keyword(s):  
Operating System ◽  
Operating Systems ◽  
Data Privacy ◽  
The Internet ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Basic Source ◽  
Basic Functions ◽  
And Performance ◽  
The Impact

The internet is a basic source of information for many specialities and uses. Such information includes sensitive data whose retrieval has been one of the basic functions of the internet. In order to protect the information from falling into the hands of an intruder, a VPN has been established. Through VPN, data privacy and security can be provided. Two main technologies of VPN are to be discussed; IPSec and Open VPN. The complexity of IPSec makes the OpenVPN the best due to the latter’s portability and flexibility to use in many operating systems. In the LAN, VPN can be implemented through Open VPN to establish a double privacy layer(privacy inside privacy). The specific subnet will be used in this paper. The key and certificate will be generated by the server. An authentication and key exchange will be based on standard protocol SSL/TLS. Various operating systems from open source and windows will be used. Each operating system uses a different hardware specification. Tools such as tcpdump and jperf will be used to verify and measure the connectivity and performance. OpenVPN in the LAN is based on the type of operating system, portability and straightforward implementation. The bandwidth which is captured in this experiment is influenced by the operating system rather than the memory and capacity of the hard disk. Relationship and interoperability between each peer and server will be discussed. At the same time privacy for the user in the LAN can be introduced with a minimum specification.

Protection of Data Privacy in Computer Systems

1986 ◽  
Vol 21 (1) ◽  
pp. 5-14
Author(s):  
Benjamin G. Walker
Keyword(s):  
Data Privacy ◽  
Computer Systems ◽  
Cost Effective ◽  
Diagnostic Information ◽  
Sensitive Data ◽  
Technical Problems ◽  
System Failures ◽  
Telephone Conversation ◽  
Computer Based ◽  
Maintenance Personnel

The protection of data in computer-based systems is a serious and growing problem. It is one of the most challenging technical problems in the field of computer science today. The objective of this paper is to provide a technical overview of the problem and to suggest some steps that need to be taken to assure progress in the field toward cost-effective systems that provide adequate protection.The Problem: Protecting the privacy of data in computer systems involves establishing safeguards against accidental disclosure as well as protection against a deliberate attack. During system failures and restart procedures errors in coding procedures often cause data to be stored in the wrong files or put sensitive data out on the printer along with diagnostic information intended for maintenance personnel. You have probably had the experience at some time of being wired into someone else's telephone conversation.

Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ashutosh Shankhdhar ◽  
Pawan Kumar Verma ◽  
Prateek Agrawal ◽  
Vishu Madaan ◽  
Charu Gupta
Keyword(s):  
Human Brain ◽  
Network Architecture ◽  
Neural Control ◽  
Human Life ◽  
Binary Classification ◽  
Sampling Rate ◽  
Quality Analysis ◽  
Eeg Signal ◽  
Content Type

PurposeThe aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.Design/methodology/approachThis paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.FindingsAt the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.Originality/valueBCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

Towards Device Independent Eavesdropping on Telephone Conversations with Built-in Accelerometer

2021 ◽  
Vol 5 (4) ◽  
pp. 1-29
Author(s):  
Weigao Su ◽  
Daibo Liu ◽  
Taiyuan Zhang ◽  
Hongbo Jiang
Keyword(s):  
Sampling Rate ◽  
Physical Contact ◽  
Motion Sensors ◽  
Telephone Conversation ◽  
Root Cause ◽  
Phone Calls ◽  
One Step ◽  
Speech Information ◽  
The Impact ◽  
The Voice

Motion sensors in modern smartphones have been exploited for audio eavesdropping in loudspeaker mode due to their sensitivity to vibrations. In this paper, we further move one step forward to explore the feasibility of using built-in accelerometer to eavesdrop on the telephone conversation of caller/callee who takes the phone against cheek-ear and design our attack Vibphone. The inspiration behind Vibphone is that the speech-induced vibrations (SIV) can be transmitted through the physical contact of phone-cheek to accelerometer with the traces of voice content. To this end, Vibphone faces three main challenges: i) Accurately detecting SIV signals from miscellaneous disturbance; ii) Combating the impact of device diversity to work with a variety of attack scenarios; and iii) Enhancing feature-agnostic recognition model to generalize to newly issued devices and reduce training overhead. To address these challenges, we first conduct an in-depth investigation on SIV features to figure out the root cause of device diversity impacts and identify a set of critical features that are highly relevant to the voice content retained in SIV signals and independent of specific devices. On top of these pivotal observations, we propose a combo method that is the integration of extracted critical features and deep neural network to recognize speech information from the spectrogram representation of acceleration signals. We implement the attack using commodity smartphones and the results show it is highly effective. Our work brings to light a fundamental design vulnerability in the vast majority of currently deployed smartphones, which may put people's speech privacy at risk during phone calls. We also propose a practical and effective defense solution. We validate that it is feasible to prevent audio eavesdropping by using random variation of sampling rate.

scholarly journals PRISER: Managing Notification in Multiples Devices with Data Privacy Support

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3098 ◽  
Author(s):  
Luis Augusto Silva ◽  
Valderi Reis Quietinho Leithardt ◽  
Carlos O. Rolim ◽  
Gabriel Villarrubia González ◽  
Cláudio F. R. Geyer ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Management System ◽  
Data Privacy ◽  
Smart Devices ◽  
User Profiles ◽  
Status Data ◽  
Short Time

With the growing number of mobile devices receiving daily notifications, it is necessary to manage the variety of information produced. New smart devices are developed every day with the ability to generate, send, and display messages about their status, data, and information about other devices. Consequently, the number of notifications received by a user is increasing and their tolerance may decrease in a short time. With this, it is necessary to develop a management system and notification controls. In this context, this work proposes a notification and alert management system called PRISER. Its focus is on user profiles and environments, applying data privacy criteria.

Privacy-Preserving Outsourced Similarity Search

2014 ◽  
Vol 25 (3) ◽  
pp. 48-71 ◽  
Author(s):  
Stepan Kozak ◽  
David Novak ◽  
Pavel Zezula
Keyword(s):  
Similarity Search ◽  
Data Privacy ◽  
System Analysis ◽  
Keyword Search ◽  
Evaluation Criteria ◽  
Similarity Index ◽  
Data Retrieval ◽  
Privacy Preserving ◽  
Scientific Data ◽  
Sensitive Data

The general trend in data management is to outsource data to 3rd party systems that would provide data retrieval as a service. This approach naturally brings privacy concerns about the (potentially sensitive) data. Recently, quite extensive research has been done on privacy-preserving outsourcing of traditional exact-match and keyword search. However, not much attention has been paid to outsourcing of similarity search, which is essential in content-based retrieval in current multimedia, sensor or scientific data. In this paper, the authors propose a scheme of outsourcing similarity search. They define evaluation criteria for these systems with an emphasis on usability, privacy and efficiency in real applications. These criteria can be used as a general guideline for a practical system analysis and we use them to survey and mutually compare existing approaches. As the main result, the authors propose a novel dynamic similarity index EM-Index that works for an arbitrary metric space and ensures data privacy and thus is suitable for search systems outsourced for example in a cloud environment. In comparison with other approaches, the index is fully dynamic (update operations are efficient) and its aim is to transfer as much load from clients to the server as possible.

Sign in / Sign up

Export Citation Format

Share Document