scholarly journals A Survey: Data Leakage Detection Techniques

2018 ◽  
Vol 8 (4) ◽  
pp. 2247
Author(s):  
K. S. Wagh
Keyword(s):  
Credit Card ◽  
Information Leakage ◽  
Leakage Detection ◽  
Sensitive Data ◽  
Detection Techniques ◽  
Personal Credit ◽  
Customer Information ◽  
Other Information ◽  
Effective System ◽  
Credit Card Data

Data is an important property of various organizations and it is intellectual property of organization. Every organization includes sensitive data as customer information, financial data, data of patient, personal credit card data and other information based on the kinds of management, institute or industry. For the areas like this, leakage of information is the crucial problem that the organization has to face, that poses high cost if information leakage is done. All the more definitely, information leakage is characterize as the intentional exposure of individual or any sort of information to unapproved outsiders. When the important information is goes to unapproved hands or moves towards unauthorized destination. This will prompts the direct and indirect loss of particular industry in terms of cost and time. The information leakage is outcomes in vulnerability or its modification. So information can be protected by the outsider leakages. To solve this issue there must be an efficient and effective system to avoid and protect authorized information. From not so long many methods have been implemented to solve same type of problems that are analyzed here in this survey.  This paper analyzes little latest techniques and proposed novel Sampling algorithm based data leakage detection techniques.

Identifying Major Issues of Tourism Analysis Using Big Data : Focused on Mobile and Credit Card Data

2018 ◽  
Vol 30 (3) ◽  
pp. 3-22
Author(s):  
Won-Seop Shim ◽  
Seung-Mook Choi ◽  
Chang-Sup Shim
Keyword(s):  
Big Data ◽  
Credit Card ◽  
Credit Card Data

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

scholarly journals Neural mechanisms of credit card spending

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sachin Banker ◽  
Derek Dunfield ◽  
Alex Huang ◽  
Drazen Prelec
Keyword(s):  
Credit Card ◽  
Credit Cards ◽  
Purchase Behavior ◽  
Facilitation Effect ◽  
Product Price ◽  
Personal Credit ◽  
Psychological Mechanisms ◽  
Network Activation ◽  
Payment Instruments ◽  
Reward Network

AbstractCredit cards have often been blamed for consumer overspending and for the growth in household debt. Indeed, laboratory studies of purchase behavior have shown that credit cards can facilitate spending in ways that are difficult to justify on purely financial grounds. However, the psychological mechanisms behind this spending facilitation effect remain conjectural. A leading hypothesis is that credit cards reduce the pain of payment and so ‘release the brakes’ that hold expenditures in check. Alternatively, credit cards could provide a ‘step on the gas,’ increasing motivation to spend. Here we present the first evidence of differences in brain activation in the presence of real credit and cash purchase opportunities. In an fMRI shopping task, participants purchased items tailored to their interests, either by using a personal credit card or their own cash. Credit card purchases were associated with strong activation in the striatum, which coincided with onset of the credit card cue and was not related to product price. In contrast, reward network activation weakly predicted cash purchases, and only among relatively cheaper items. The presence of reward network activation differences highlights the potential neural impact of novel payment instruments in stimulating spending—these fundamental reward mechanisms could be exploited by new payment methods as we transition to a purely cashless society.

scholarly journals The COVID-19 Shock and Consumer Credit: Evidence from Credit Card Data

2021 ◽  
Vol 2021 (008) ◽  
pp. 1-55
Author(s):  
Akos Horvath ◽  
◽  
Benjamin Kay ◽  
Carlo Wix ◽  
◽  
...  
Keyword(s):  
Interest Rates ◽  
Credit Card ◽  
Negative Impact ◽  
Consumer Credit ◽  
Credit Cards ◽  
Sharp Decrease ◽  
Main Driver ◽  
Credit Card Data ◽  
Pandemic Severity ◽  
The Impact

We use credit card data from the Federal Reserve Board's FR Y-14M reports to study the impact of the COVID-19 shock on the use and availability of consumer credit across borrower types from March through August 2020. We document an initial sharp decrease in credit card transactions and outstanding balances in March and April. While spending starts to recover by May, especially for risky borrowers, balances remain depressed overall. We find a strong negative impact of local pandemic severity on credit use, which becomes smaller over time, consistent with pandemic fatigue. Restrictive public health interventions also negatively affect credit use, but the pandemic itself is the main driver. We further document a large reduction in credit card originations, especially to risky borrowers. Consistent with a tightening of credit supply and a flight-to-safety response of banks, we find an increase in interest rates of newly issued credit cards to less creditworthy borrowers.

scholarly journals Smart health cards or health smart cards?

2009 ◽  
Vol 33 (1) ◽  
pp. 3
Keyword(s):  
Social Services ◽  
Smart Card ◽  
Credit Card ◽  
New Technologies ◽  
Business Case ◽  
Smart Cards ◽  
Easy Access ◽  
Health And Social Services ◽  
Other Information ◽  
The Government

THERE IS PLENTY OF ACTIVITY throughout the world focusing on encrypting personal health (and other) information on credit card-sized plastic ?smart? cards. These cards are embedded with a computer chip and could provide easy access to essential health information. As with many new technologies, there is debate about smart cards in health. In July 2004 the Federal Minister for Health and Ageing at that time, the Hon Tony Abbott, announced that ?Australians will have access to a new Medicare smart card as part of the government?s electronic health agenda to improve the quality and accessibility of patient information across the health system?.1 This led to the introduction of the Health and Social Services smart card initiative. The business case for this initiative suggested that this card could replace around 17 government issued ?health? cards, while improving proof of identify arrangements.2 While in opposition, the Labor Party opposed the notion of the smart card, claiming it was an identity card by stealth,3 and at the time of writing, it appears that the health smart card has been put on the backburner while the Government sorts out the priorities. In this issue, Mohd Rosli and his Melbourne colleagues report on a study of patient and staff perceptions about health smart cards (page 136). In this study, 270 emergency department patients and 92 staff completed self-administered questionnaires. The findings among patients and staff generally supported the introduction of smart cards with the majority reporting that the advantages outweighed the disadvantages. The majority of the respondents indicated that the cards should be brought into use, and that they would use one if offered. However, the study did find that a large proportion of staff and patients were not aware of health smart cards at all. A fundamental change in the structure of our relationship with the government had been proposed through the Health and Social Services smart card initiative, and yet the findings of this study suggest that the Australian public was ill prepared to discuss the implications. Where is the information sharing, the discussion and the debate that can help shape our health care system for the future? In our last issue of 2008 we included a call for student papers. I would like to remind all readers of this important initiative, reproduced overleaf, as I believe this is an effective way to begin to encourage the necessary discussion and debate.

scholarly journals Fraudulent credit card transaction detection using soft computing techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 1634
Author(s):  
Aishwarya Priyadarshini ◽  
Sanhita Mishra ◽  
Debani Prasad Mishra ◽  
Surender Reddy Salkuti ◽  
Ramakanta Mohanty
Keyword(s):  
Soft Computing ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Detection System ◽  
Fraud Detection ◽  
K Nearest Neighbor ◽  
Computationally Efficient ◽  
Detection Techniques ◽  
Government Organizations ◽  
Soft Computing Techniques

<p>Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.</p>

scholarly journals Semi-Automatic Locating of Cryptographic Operations in Side-Channel Traces

2021 ◽  
pp. 345-366
Author(s):  
Jens Trautmann ◽  
Arthur Beckers ◽  
Lennert Wouters ◽  
Stefan Wildermann ◽  
Ingrid Verbauwhede ◽  
...  
Keyword(s):  
Fault Injection ◽  
Side Channel ◽  
Leakage Detection ◽  
Clock Frequency ◽  
Detection Techniques ◽  
Effective Time ◽  
Meta Information ◽  
Software Implementations ◽  
Single Data ◽  
The Time Domain

Locating a cryptographic operation in a side-channel trace, i.e. finding out where it is in the time domain, without having a template, can be a tedious task even for unprotected implementations. The sheer amount of data can be overwhelming. In a simple call to OpenSSL for AES-128 ECB encryption of a single data block, only 0.00028% of the trace relate to the actual AES-128 encryption. The rest is overhead. We introduce the (to our best knowledge) first method to locate a cryptographic operation in a side-channel trace in a largely automated fashion. The method exploits meta information about the cryptographic operation and requires an estimate of its implementation’s execution time.The method lends itself to parallelization and our implementation in a tool greatly benefits from GPU acceleration. The tool can be used offline for trace segmentation and for generating a template which can then be used online in real-time waveformmatching based triggering systems for trace acquisition or fault injection. We evaluate it in six scenarios involving hardware and software implementations of different cryptographic operations executed on diverse platforms. Two of these scenarios cover realistic protocol level use-cases and demonstrate the real-world applicability of our tool in scenarios where classical leakage-detection techniques would not work. The results highlight the usefulness of the tool because it reliably and efficiently automates the task and therefore frees up time of the analyst.The method does not work on traces of implementations protected by effective time randomization countermeasures, e.g. random delays and unstable clock frequency, but is not affected by masking, shuffling and similar countermeasures.

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

2019 ◽  
pp. 188-212
Author(s):  
Suriya Murugan ◽  
Anandakumar H.
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Private Information ◽  
Online Social Networks ◽  
Information Leakage ◽  
User Profile ◽  
User Privacy ◽  
Sensitive Data ◽  
Data Set ◽  
Secure Information

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.

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