Data Breach – Its Effects on Industry

In this Digital world, Data has become one of the most crucial parts in every field. To protect this sensitive piece of information many methods and technologies are coming into existence. A data breach reveals sensitive, protected and confidential information to an unauthorized person. Increasingly opportunities exist for information to leak out as our computers and mobile devices become more associated. Data leaks pose a serious threat to companies and can cost them significantly either financially and reputationally. The long-term effects of a data breach can spread throughout a company, having an effect on all parties involved, including the user base, staff, and cybersecurity teams in charge of repair. By giving priority to the most frequently attacked industries, this article will advance understanding about data hacking incidents and aid in securing corporate data

Privacy Implications of Health Information Seeking on the Web

This article investigates privacy risks to those visiting health- related web pages. The population of pages analyzed is derived from the 50 top search results for 1,986 common diseases. This yielded a total population of 80,124 unique pages which were analyzed for the presence of third-party HTTP requests. 91% of pages were found to make requests to third parties. Investigation of URIs revealed that 70% of HTTP Referer strings contained information exposing specific conditions, treatments, and diseases. This presents a risk to users in the form of personal identification and blind discrimination. An examination of extant government and corporate policies reveals that users are insufficiently protected from such risks

Data Leaks Detection Mechanism for Small Businesses

The protection of sensitive customer information is a vital responsibility for companies of all sizes. In modern times, there is a significant need for not only protecting the data that is being shared but also gaining knowledge of its leakage points and the circumstances under which it is compromised. After locating the location where data is being lost, it is necessary to identify the person responsible for the breach. When it comes to protecting a company from suffering significant financial damage because of data leakage throughout the course of normal business operations, it is very essential to have a solid understanding of the individuals who are responsible for leaking the data. This study tries to discover how small firms might be assisted in protecting the sensitive information that they own. This study\u27s objective is to determine how sites of companies react to attacks that are damaging to their operations so that appropriate action may be taken

Privacy Violation and Detection Using Pattern Mining Techniques

Privacy, its violations and techniques to bypass privacy violation have grabbed the centre-stage of both academia and industry in recent months. Corporations worldwide have become conscious of the implications of privacy violation and its impact on them and to other stakeholders. Moreover, nations across the world are coming out with privacy protecting legislations to prevent data privacy violations. Such legislations however expose organizations to the issues of intentional or unintentional violation of privacy data. A violation by either malicious external hackers or by internal employees can expose the organizations to costly litigations. In this paper, we propose PRIVDAM; a data mining based intelligent architecture of a Privacy Violation Detection and Monitoring system whose purpose is to detect possible privacy violations and to prevent them in the future. Experimental evaluations show that our approach is scalable and robust and that it can detect privacy violations or chances of violations quite accurately. Please contact the author for full text at [email protected]

Adaptive N-Gram Classifier for Privacy Protection

We are living in a world where information is worth more than gold. Hence protecting sensitive information has become a crucial task. When telephones gave way to smartphones people not just start using them as communication tools, but to work on the go and to actively immerse in social network circles and other private communication services like chat SMS etc. Knowing each end point to the Internet is a potential risk which was a PC or laptop a while ago. Traditional methods limit the usage and somewhat the convenience of the user which dealt severely. The user knowingly or unknowingly releases sensitive information into the web which are either monitored or mined by third parties and uses them for unlawful purposes. Existing techniques mostly use data fingerprinting, exact and partial document matching and statistical methods to classify sensitive data. Keyword-based are used when the target documents are less diverse and they ignore the context of the keyword, on the other hand statistical methods ignore the content of the analyzed text. In this paper we propose a dynamic N-gram analyzer which can be used as a document classifier, we investigate the relationship of size and quality of N-grams and the effect of other feature sets like exclusion of common N-grams, grammatical words, N-gram-sizes etc. Another improvement is in the area of dynamic N-gram updater which dynamically changes the N-gram feature vectors. Our work has shown that the techniques fairly outperforms the traditional methods even when the categories exhibit frequent similarities. DOI: 10.17762/ijritcc2321-8169.150614