Profile Based Personalized Web Search

S.No. Method Prediction Rate 1. Web Page Prefetching 0.65 2. AR,FS, and FGS 0.69 3. Sequential Association Rule 0.72 4.

In the past decade, a number of PPDM techniques have been proposed to facilitate users in performing data mining tasks in privacy-sensitive environments. Agrawal and Srikant [3], as well as Lindell and Pinkas [63], were the first to introduce the notion of privacy-preserving under data mining applications. Existing PPDM techniques can be classified into two broad categories: data perturbation and data distribution. Data Perturbation Methods: With these methods, values of individual data records are perturbed by adding random noise in such a way that the distribution of the perturbed data look very deferent from that of the actual data. After such a transformation, the perturbed data is sent to the Miner to perform the desired data mining tasks. Agrawal and Srikant [3] proposed the first data perturbation technique that could be used to build a decision-tree classifier. A number of randomization-based methods were later proposed [6, 33, 34, 73, 104]. Data perturbation techniques are not, however, applicable to semantically- secure encrypted data. They also fail to produce accurate data mining results due to the addition of statistical noises to the data. Data Distribution Methods: These methods assume that the dataset is partitioned eitherhorizontallyorverticallyanddistributedacrossdifferentparties. The parties

I found the technique of the permuterm indexing especially of note. While it seems like it results in a massive index it was interesting to think of how that could be applied along with other ideas to mitigate human imperfections for searches.

Privacy is something that most people believe is not possible on the internet, but with the correct knowledge it can be possible. In Nicholas Carr’s essay “Tracking Is an Assault on Liberty”, he states that “It is very easy to find information about people on the internet, even private things that people don’t expect others to be able to see” (538). People don’t realize that what they do online can affect their personal lives such as their credit score, the ads that are recommended to them, and even the cookies in their computer. While Carr may have great points, he may not have considered the ways people do have privacy. There are some ways to protect browsing, people just need to know how. Most browsers have a mode that allows people to visit sites without being tracked. There’s no history, and no cookies.

Whenever you are talking about privacy on the internet it is something that can't be taken lightly. With one click all of your information is put into a network, and you never know who’s hands it could end up in. First you must always question if a page needs your information. Why would they need to know who you are? Second, if you do happen to give them your information you must also consider where that information will go or end up.

Privacy in this era is threatened by the growth in technology with capacity that is enhanced for surveillance, storage, communication as well as computation. Moreover, the increased value of this information in decision making is one of the insidious threats. For this reason, information and its privacy are actually threatened and less privacy is assured.

I have decided to write a research paper on the importance of protecting personally identifiable information (PII) in Information Technology. PII is a critical, but often overlooked skill requirement for IT professionals. The subject of PII data is of vital importance to me since I work with PII data frequently and must be prepared to handle it correctly and ethically, less risk the violation of privacy law. In addition to satisfying the necessary requirements for a research paper, the intention of this paper are to provide:

Congress as a bill in January, 2003 (see H.R. 69).  Even though OPPA is just proposed legislation at this point, it encompasses most of the necessary components for comprehensive protection of privacy online called for by privacy advocates and entities such as the FTC. It is also consistent with the Fair Information Practices (FIP) [9], which have operated as a guide for policy makers in the U.S. If the U.S. does indeed enact comprehensive online privacy legislation, it will most likely continue to use the FIPs as a guide and therefore, will closely resemble OPPA.   The results presented herein will benefit managers and website designers of companies involved in international business, as well as policy makers.

Over the past few years, the development of the Internet and the intrusive surveillance capabilities of these technologies have caused privacy to become a major political and social issue for millions of Americans who go online.  Companies employ a variety of tools to gather marketable information on American citizens.  Most of the use of this information is for personalized advertisement and to create databases of target audiences.  While these activities may appear to be nothing more than annoyances for a majority of Americans, there is the hidden danger of the loss of privacy.

Privacy either encourages or is a necessary factor of human securities and fundamental value such as human embarrassment, independence, distinctiveness, freedom, and public affection. Being completely subject to mutual scrutiny will begin to lose self-respect, independence, distinctiveness, and freedom as a result of the sometimes strong burden to conform to public outlooks.

In my opinion, the meaning of privacy of our personal data when we use online services on the Internet is different from what Google and other Internet companies are interpreting the meaning of Privacy to suit their business need to generate advertisement revenue by allowing companies to display advertisement relevant to the web search by their customer on their website.

In an encounter between a user and a service, the service requests some personally identifiable information (PII1) from the user, and the user may agree or disagree to the disclosure. Disclosure depends on the service’s properties and its privacy policy, a document that details how the service is going to handle users’ data. To automate this decision process, the policy is written in S4P, a formal language that machines can interpret. Furthermore, the user also has a document written in S4P, called preference, which specifies her requirements on the service’s properties and on its policy for this

Personal privacy today is a controversial and complex topic, which is influenced by a number of factors. There is an integral role that databases play in this highly debated topic. The fact that many people now carry out their transactions electronically is another important factor. There is also pressure on personal privacy for increased national security around the world to combat terrorism. In addition, personal privacy is even threatened by commercial factors and the Internet.

The information of users search interests are stored offline and a profile based system is maintained for each and every user so that accurate results are obtained for different users working on the same device. Keywords— Smartphone image recommender, image recommender, profile base image recommender, image re-ranking. I. INTRODUCTION The primary objective of this paper is to provide accurate search results based on keyword expansion as well as comparing the semantic signatures of images to provide re-ranked images for the android operating system.

Various techniques have been used in content-based models. Such systems try to find regularities in the descriptions that can be used to distinguish highly rated items from others [97]. Content-based approaches are based on objective information about the items. This information is automatically extracted from various sources (e.g., Web pages) or manually introduced (e.g., product database). However, selecting one item or another is based mostly on subjective attributes of the item (e.g., a well-written document or a product with a spicy taste). Therefore, these attributes, which better influence the user’s choice, are not taken into account. In the rest of this section, we discuss three technique of content-based filtering technique including keyword-based models, semantic techniques, and probabilistic models. The first systematic evaluation of the impact of applying perturbation-based privacy technologies on the usability of content-based recommendation systems proposed by Puglisi, S., et al. (2015) [98]. The primary goal of their work is to investigate the effects of tag forgery to the content-based recommendation in a real-world application scenario, studying the interplay between the degree of privacy and the potential degradation of the quality of the recommendation. In other paper, Rana, C. and S.K. Jain [23] have developed a book recommendation system that is based on content-based recommendation technique and takes into account the choices of not an