Protecting Digital Data Privacy in Computer Forensic Examination

2011 ◽  
Author(s):  
Frank Y.W. Law ◽  
Patrick P.F. Chan ◽  
S.M. Yiu ◽  
K.P. Chow ◽  
Michael Y.K. Kwan ◽  
...  
Keyword(s):  
Data Privacy ◽  
Digital Data ◽  
Forensic Examination ◽  
Computer Forensic

Computer Forensic Investigation in Cloud of Things

2019 ◽  
pp. 256-270
Author(s):  
A. Surendar
Keyword(s):  
Smart Cities ◽  
Wearable Devices ◽  
Digital Data ◽  
Challenging Problem ◽  
Smart Vehicles ◽  
Healthcare Data ◽  
Iot Devices ◽  
Smart Governance ◽  
Computer Forensic ◽  
Individual Healthcare

Digital data transformation is most challenging in developing countries. In recent days, all the applications are functioning with the support of internet of things (IoT). Wearable devices involve the most insightful information, which includes individual healthcare data. Health records of patients must be protected. IoT devices could be hacked, and criminals use this information. Smart cities with IoT use information technology to collect, analyze, and integrate information. Smart reduces the network traffic using the ground sensors, micro-radars, and drones monitor traffic to the traffic controller based on that signals are designed. The data collected includes the images and convey information to smart vehicles, which in turn, if data are hacked, may affect many people. Smart city includes important features such as smart buildings, smart technology, smart governance, smart citizen, and smart security. Cyber threat is a challenging problem, and usage of apps may increase malware that affects various customers.

A Generic Privacy Breach Preventing Methodology for Cloud Based Web Service

2015 ◽  
pp. 426-458 ◽  
Author(s):  
S. R. Murugaiyan ◽  
D. Chandramohan ◽  
T. Vengattaraman ◽  
P. Dhavachelvan
Keyword(s):  
Private Information ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Digital Data ◽  
Cloud Data ◽  
Privacy Breach ◽  
Cloud Data Storage ◽  
User Data ◽  
Information Technology Development ◽  
User Data Privacy

The present focuses on the Cloud storage services are having a critical issue in handling the user's private information and its confidentiality. The User data privacy preserving is a vital facet of online storage in cloud computing. The information in cloud data storage is underneath, staid molests of baffling addict endeavor, and it may leads to user clandestine in a roar privacy breach. Moreover, privacy preservation is an indeed research pasture in contemporary information technology development. Preserving User Data in Cloud Service (PUDCS) happens due to the data privacy breach results to a rhythmic way of intruding high confidential digital storage area and barter those information into business by embezzle others information. This paper focuses on preventing (hush-hush) digital data using the proposed privacy preserving framework. It also describes the prevention of stored data and de-identifying unauthorized user attempts, log monitoring and maintaining it in the cloud for promoting allusion to providers and users.

Towards Distributed Association Rule Mining Privacy

2011 ◽  
pp. 245-271
Author(s):  
Mafruz Ashrafi ◽  
David Taniar ◽  
Kate Smith
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Large Data ◽  
Digital Data ◽  
Sensitive Information ◽  
Distributed Data ◽  
Data Repositories ◽  
Actionable Knowledge ◽  
The Cost ◽  
Network Technologies

With the advancement of storage, retrieval, and network technologies today, the amount of information available to each organization is literally exploding. Although it is widely recognized that the value of data as an organizational asset often becomes a liability because of the cost to acquire and manage those data is far more than the value that is derived from it. Thus, the success of modern organizations not only relies on their capability to acquire and manage their data but their efficiency to derive useful actionable knowledge from it. To explore and analyze large data repositories and discover useful actionable knowledge from them, modern organizations have used a technique known as data mining, which analyzes voluminous digital data and discovers hidden but useful patterns from such massive digital data. However, discovery of hidden patterns has statistical meaning and may often disclose some sensitive information. As a result, privacy becomes one of the prime concerns in the data-mining research community. Since distributed data mining discovers rules by combining local models from various distributed sites, breaching data privacy happens more often than it does in centralized environments.

Privacy Preserving Machine Learning and Deep Learning Techniques

2020 ◽  
pp. 222-235
Author(s):  
Divya Asok ◽  
Chitra P. ◽  
Bharathiraja Muthurajan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Privacy ◽  
Differential Privacy ◽  
Digital Data ◽  
Sensitive Data ◽  
Security Issues ◽  
Private Data ◽  
Learning Techniques ◽  
Machine Learning Applications

In the past years, the usage of internet and quantity of digital data generated by large organizations, firms, and governments have paved the way for the researchers to focus on security issues of private data. This collected data is usually related to a definite necessity. For example, in the medical field, health record systems are used for the exchange of medical data. In addition to services based on users' current location, many potential services rely on users' location history or their spatial-temporal provenance. However, most of the collected data contain data identifying individual which is sensitive. With the increase of machine learning applications around every corner of the society, it could significantly contribute to the preservation of privacy of both individuals and institutions. This chapter gives a wider perspective on the current literature on privacy ML and deep learning techniques, along with the non-cryptographic differential privacy approach for ensuring sensitive data privacy.

Preserving Data Privacy in Electronic Health Records Using Blockchain Technology

2021 ◽  
pp. 1222-1230
Author(s):  
Sathiyabhama B. ◽  
Rajeswari K. C. ◽  
Reenadevi R. ◽  
Arul Murugan R.
Keyword(s):  
Electronic Health Records ◽  
Data Privacy ◽  
Digital Data ◽  
Healthcare Industry ◽  
Health Issues ◽  
Health Records ◽  
Blockchain Technology ◽  
The People ◽  
Predominant Factor ◽  
Electronic Health

Technology is a boon to mankind in this fast-growing era. The advancement in technology is the predominant factor for the sophisticated way of living of the people. In spite of technology, revolution happens across the world, and mankind still suffers due to various health issues. Healthcare industries take immense measures to improve the quality of life. An enormous volume of digital data is being handled every day in the healthcare industry. There arises a need for the intervention of technology in the healthcare industry to be taken to a greater extent. The prime duty of any healthcare industry is to store and maintain those data in the form of electronic health records (EHR) in a secured manner.

Analisis Kelayakan Integrated Digital Forensics Investigation Framework Untuk Investigasi Smartphone

2016 ◽  
Vol 7 (4) ◽  
Author(s):  
Ruuhwan Ruuhwan ◽  
Imam Riadi ◽  
Yudi Prayudi
Keyword(s):  
Digital Forensics ◽  
Electronic Media ◽  
Digital Data ◽  
Digital Evidence ◽  
Forensic Investigation ◽  
Digital Forensic ◽  
Forensic Examination ◽  
Different Types ◽  
Valid Evidence

Abstract. The handling of digital evidence each and every digital data that can proof a determination that a crime has been committed; it may also give the links between a crime and its victims or crime and the culprit. How to verify a valid evidence is to investigate using the approach known as the Digital Forensic Examination Procedures. Integrated Digital Forensic Investigation Framework (IDFIF) is the latest developed method, so that it is interesting to further scrutinize IDFIF, particularly in the process of investigation of a smartphone. The current smartphone devices have similar functions with computers. Although its functions are almost the same as the computer, but there are some differences in the process of digital forensics handling between computer devices and smartphones. The digital evidence handling process stages need to overcome the circumstances that may be encountered by an investigator involving digital evidence particularly on electronic media and smartphone devices in the field. IDFIF needs to develop in such a way so it has the flexibility in handling different types of digital evidence.Keywords: digital evidence, IDFIF, investigation, smartphoneAbstraks. Penanganan bukti digital mencakup setiap dan semua data digital yang dapat menjadi bukti penetapan bahwa kejahatan telah dilakukan atau dapat memberikan link antara kejahatan dan korbannya atau kejahatan dan pelakunya. Cara pembuktian untuk mendapatkan bukti valid adalah dengan melakukaninvestigasi dengan pendekatan Prosedur Pemeriksaan Digital Forensic. Integrated Digital Forensics Investigation Framework (IDFIF) merupakan metode terbaru sehingga IDFIF ini menarik untuk diteliti lebih lanjut terutama dalam proses investigasi smartphone. Saat ini perangkat smartphone memiliki fungsi yang sama dengan komputer. Meskipun demikian, ada beberapa perbedaan dalam proses penanganan digital forensics diantara perangkat komputer dan smartphone. Tahapan proses penanganan barang bukti digital seharusnya dibuat untuk mengatasi keadaan umum yang mungkin dihadapi oleh investigator yangmelibatkan barang bukti digital terutama pada perangkat smartphone dan media elektronik terkait di lapangan. IDFIF perlu dikembangkan sehingga memiliki fleksibilitas dalam menangani berbagai jenis barang bukti digital.Kata Kunci: bukti digital, IDFIF, investigasi, smartphone

University Student’s Perceptions of Self-Tracking Devices, Data Privacy, and Sharing Digital Data for Research Purposes

2020 ◽  
Vol 3 (2) ◽  
pp. 128-134 ◽  
Author(s):  
Marianne I. Clark ◽  
Matthew W. Driller
Keyword(s):  
Physical Activity ◽  
University Students ◽  
Data Privacy ◽  
Best Practice ◽  
Research Process ◽  
Digital Data ◽  
Activity Monitors ◽  
Best Practice Guidelines ◽  
Tracking Device ◽  
Tracking Devices

Purpose: Wearable physical activity monitors present new ethical considerations for researchers and research ethics boards. Best practice guidelines are needed for research involving wearable monitors and should consider how these devices may impact participants outside of the research context. This study examines the perceptions of university students who wore activity monitors for research purposes in order to inform such guidelines. Methods: Focus groups were held with university students who wore digital self-tracking devices for a study examining sleep and physical activity. Questions focused on motivations to wear a physical activity monitor for research, understandings of how personal digital data generated by self-tracking devices are used and accessed, and perceptions of privacy. Results: 83% of students trusted the research process and were motivated to contribute to scientific knowledge by wearing a digital tracking device. Most (83%) understood how their data were used and accessed for research purposes, but 79% were less clear on how data might be accessed and used by third parties. 79% of participants also agreed that different data carries different social and personal implications and thus should not be treated the same by researchers. Conclusions: Protocols for research involving wearable monitors should include briefing/debriefing sessions to clarify data privacy, storage, and use issues. Researchers should also consider how wearing these devices might prompt unexpected emotional and other responses and the social implications of use for participants. The concept of privacy requires further exploration in the context of digital data collection using commercial devices.

scholarly journals The future of value in digitalised higher education: why data privacy should not be our biggest concern

2020 ◽  
Author(s):  
Janja Komljenovic
Keyword(s):  
Higher Education ◽  
Global Economy ◽  
Data Privacy ◽  
Political Action ◽  
Explanatory Power ◽  
Research Programme ◽  
Digital Data ◽  
Data Value ◽  
Public Scrutiny ◽  
New Research

AbstractUniversities around the world are increasingly digitalising all of their operations, with the current COVID-19 pandemic speeding up otherwise steady developments. This article focuses on the political economy of higher education (HE) digitalisation and suggests a new research programme. I foreground three principal arguments, which are empirically, theoretically, and politically crucial for HE scholars. First, most literature is examining the impacts of digitalisation on the HE sector and its subjects alone. I argue that current changes in digitalising HE cannot be studied in isolation from broader changes in the global economy. Specifically, HE digitalisation is embedded in the expansion of the digital economy, which is marked by new forms of value extraction and rentiership. Second, the emerging research on the intersection of marketisation and digitalisation in HE seems to follow the theories of marketisation qua production and commodification. I argue that we need theories with better explanatory power in analysing the current digitalisation dynamics. I propose to move from commodification to assetisation, and from prices to rents. Finally, universities are digitalising in the time when the practice is superseding policy, and there is no regulation beyond the question of data privacy. However, digital data property is already a reality, governed by ‘terms of use’, and protected by the intellectual property rights regime. The current pandemic has led to ‘emergency pedagogy’, which has intensified overall digitalisation in the sector and is bypassing concerns of data value redistribution. I argue that we urgently need public scrutiny and political action to address issues of value extraction and redistribution in HE.

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