Australia ∙ ‘Personal Information’ in the Australian Privacy Act and the Classification of IP Addresses

2017 ◽  
Vol 3 (4) ◽  
pp. 528-533
Author(s):  
J. Wagner ◽  
N. Witzleb
Keyword(s):  
Personal Information ◽  
Ip Addresses ◽  
Privacy Act

scholarly journals Malware Classification with Improved Convolutional Neural Network Model

2020 ◽  
Vol 12 (6) ◽  
pp. 30-43
Author(s):  
Sumit S. Lad ◽  
◽  
Amol C. Adamuthe
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Personal Information ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Scale ◽  
Malware Classification

Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

Systematic Government Access to Private-Sector Data in Canada

2017 ◽  
Author(s):  
Jane Bailey ◽  
Sara Shayan
Keyword(s):  
Law Enforcement ◽  
Private Sector ◽  
Personal Information ◽  
Search And Seizure ◽  
Law Enforcement Agencies ◽  
Electronic Documents ◽  
Constitutional Protection ◽  
Privacy Act ◽  
Canadian Law ◽  
Sector Data

This chapter focuses on Canadian law as it applies to government access to private-sector data. The Canadian Charter of Rights and Freedoms implicitly provides constitutional protection of privacy by prohibiting unreasonable search and seizure by the state (s. 8) and by limiting government intrusion on life, liberty and security of the person (s. 7). With some exceptions, the Charter requires law enforcement agencies to seek prior authorization before accessing personal information. However, Canada’s national security intelligence agencies are subject to more relaxed standards. The Privacy Act regulates federal government institutions’ relationship with personal information, whereas the private sector is regulated by the Personal Information and Protection of Electronic Documents Act. However, numerous exceptions in both statutes allow for (and in some cases encourage), information sharing between private-sector and state entities.

Limitations of personal information in an online environment

2018 ◽  
Vol 43 (4) ◽  
pp. 309-312
Author(s):  
Gabriella Shailer
Keyword(s):  
Personal Information ◽  
Online Environment ◽  
Privacy Act ◽  
Substantial Risk ◽  
The Individual

This Brief will demonstrate the provisions of the Privacy Act 1988 (Cth) that govern the usage of personal information can create substantial risk to the individual. It will accomplish this by setting out how the provisions of the Privacy Act 1988 (Cth) would not have prevented the sharing of the fitness data sourced from corporate servers in January 2018. It will explain how that published information would be classified as ‘de-identified’ data under Privacy Act 1988 (Cth). It will conclude by describing how the collection and collation of the data could form a risk to the individual.

scholarly journals Criminological And Psychological Analysis Of Cyberbullying

2019 ◽  
pp. 83-89
Author(s):  
O.Yu. Petechel
Keyword(s):  
Facial Expressions ◽  
Personal Information ◽  
Power Imbalance ◽  
Personal Property ◽  
Abusive Behavior ◽  
Role Structure ◽  
Psychological Analysis ◽  
Intentional Acts

The article is devoted to the research of the modem social phenomenon - bulling. The author analyzes the concepts and features of bulling. Summarizing the positions of scientists, the author highlights the following signs of bulling: 1) intentional acts; 2) long-term or / and systematic (repetitive) actions; 3) collective nature; 4) the role structure; 5) aggressiveness; 6) power imbalance between the victim and the aggressor; 7) bullying is a behavior; 8) has a personal purpose; 9) has no signs of selfdefense. The article offers a universal classification of types of bulling, in particular physical bulling (acts of aggressive nature (beating, pushing, striking, striking); social bullying (creating a tense atmosphere for learning, to form a superficial attitude of the group, even teachers or school staff to the victim, boycott, isolation, persecution); verbal bulling (threats, humiliation, ridicule, hostile facial expressions and gestures, abusive nicknames); economic bulling (seizure of money and material things, damage of personal property); sexual bullying (humiliating gestures, jokes of a sexual nature, coercion to certain negative actions, shooting in dressing rooms); cyberbullying (messaging, images, photos, videos, harassing, abusive behavior, hacking into personal mailboxes or accounts, corrupting personal information, abusive behavior in chats, social networks, and mobile). In addition, special attention has been paid to preventing this negative phenomenon. As a result of the research, the author proposes changes to the current legislation in the field of counteraction to billing, in particular the need to clarify the actions as long-term and systematic, as well as to provide for the possibility of conducting of bulling by a group of people or bulling against of group of people. The author considers it necessary to further define bulling as an act having a personal purpose.

scholarly journals Phishing Website Classification using Least Square Twin Support Vector Machine

2019 ◽  
Vol 9 (1) ◽  
pp. 2063-2068
Keyword(s):  
Support Vector Machine ◽  
Cyber Security ◽  
Classification Accuracy ◽  
Personal Information ◽  
Classification Problem ◽  
Least Square ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Security Issue

Phishing is one among the luring procedures used by phishing attackers in the means to abuse the personal details of clients. Phishing is earnest cyber security issue that includes facsimileing legitimate website to apostatize online users so as to purloin their personal information. Phishing can be viewed as special type of classification problem where the classifier is built from substantial number of website's features. It is required to identify the best features for improving classifiers accuracy. This study, highlights on the important features of websites that are used to classify the phishing website and form the legitimate ones by presenting a scheme Decision Tree Least Square Twin Support Vector Machine (DT-LST-SVM) for the classification of phishing website. UCI public domain benchmark website phishing dataset was used to conduct the experiment on the proposed classifier with different kernel function and calculate the classification accuracy of the classifiers. Computational results show that DT-LST-SVM scheme yield the better classification accuracy with phishing websites classification dataset

scholarly journals Time to Get Serious about Privacy Policies: The Special Case of Genetic Privacy

2014 ◽  
Vol 42 (1) ◽  
pp. 1-32
Author(s):  
Dianne Nicol ◽  
Meredith Hagger ◽  
Nola Ries ◽  
Johnathon Liddicoat
Keyword(s):  
Genetic Testing ◽  
Genetic Information ◽  
Personal Information ◽  
Genetic Privacy ◽  
Privacy Policies ◽  
Her Family ◽  
Current Regime ◽  
Direct To Consumer ◽  
Privacy Act ◽  
Special Case

Genetic information is widely recognised as being particularly sensitive personal information about an individual and his or her family. This article presents an analysis of the privacy policies of Australian companies that were offering direct-to-consumer genetic testing services in 2012–13. The results of this analysis indicate that many of these companies do not comply with the Privacy Act 1988 (Cth), and will need to significantly reassess their privacy policies now that significant new amendments to the Act have come into force. Whilst the Privacy Commissioner has increased powers under the new amendments, the extent to which these will mitigate the deficiencies of the current regime in relation to privacy practices of direct–to-consumer genetic testing companies remains unclear. Accordingly, it may be argued that a privacy code for the direct-to-consumer genetic testing industry would provide clearer standards. Alternatively it may be time to rethink whether a sui generis approach to protecting genetic information is warranted.

scholarly journals Privacy Requirements Classification Method for System Design

2019 ◽  
Vol 8 (12S2) ◽  
pp. 713-718
Keyword(s):  
Data Analysis ◽  
System Design ◽  
Personal Information ◽  
Sensitive Information ◽  
Reference Table ◽  
Privacy Requirements ◽  
Privacy Preferences ◽  
Information Classification ◽  
Classification Tool

It is important to get users’ privacy requirements through data or information classification during the system design. Currently, the citizen-centric perspective of privacy requirement is not well understood. To fill this gap a study with the objectives of to investigate citizens’ privacy requirements and need through their privacy preferences has been done. From the data analysis, the citizen-centric preferences’ set was developed based on the classification of personal and sensitive information that has been obtained through a survey of 350 respondents. The result is configured into a reference table and sensitivity classification tool respectively. Therefore, we suggested the tool to be used as a classifying method to classify sensitive and personal information for system design.

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