Privacy software

About: Privacy software is a research topic. Over the lifetime, 8597 publications have been published within this topic receiving 237304 citations.

Linear Regression Protocol for Privacy Protect

Abstract: With cloud computing, the arrival of the era of big data, privacy of data mining has become a hot topic. Data fitting as a basic tool for data mining research data fitting privacy issue is very meaningful. This paper studies the vertical distribution of data, while protecting privacy horizontal distribution of the linear regression problem, a linear regression of the privacy protocol, and protocol correctness, safety analyzes. The protocol uses MIARCL library verified by experiments, experimental results show that the scheme can solve the problem of the two parties involved in the linear regression, and the computational efficiency is acceptable.

Designing Location Privacy Mechanisms for flexibility over time and space

Abstract: With the increasing popularity of GPS-enabled handheld devices, location based services (LBS) have access to accurate location information, raising serious privacy concerns for end users. Trying to address these issues, the notion of geo-indistinguishability was recently introduced, protecting privacy by reporting to the LBS only a noisy version of the user's location. Standard geo-indistinguishability works well in the case of a sporadic use, however, under repeated use over time, constantly applying noise leads to a quick loss of privacy. In the first part of this thesis we show that correlation in the trace can be in fact exploited through a prediction function that tries to guess the new location reducing the number of sanitized positions. Another drawback of geo-indistinguishability is that it treats space in a uniform way, imposing the addition of the same amount of noise everywhere on the map. In the second part of this thesis we propose a novel elastic mechanism that adapts the level of noise to the different degrees of density of each area.

Privacy and security in genomic information

Abstract: Today, whole human genome sequencing is a reality affordable for many individuals. Technology advances in this arena are going fast, in fact it is possible that we are living the beginning of a “genomic revolution”. However, human genome contains highly sensitive information about individuals. Concerns regarding privacy and security are getting wider as technology advances ever more rapidly. The present work focuses on contributing and support the development of new security and privacy mechanisms in genomic information formats. An intended software prototype has been developed and it is presented in the current report. The tool permits reading and handling genomic information after evaluating the access requirements according to defined rules.

A Survey on Privacy Preserving Data Mining

Abstract: The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange and publication of data among various parties. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. Protecting private data is an important concern for society, several laws now require explicit consent prior to analysis of an individual's data, but its importance is not limited to individuals: corporations might also need to protect their information's privacy, even though sharing it for analysis could benefit the company. Clearly, the trade-off between sharing information for analysis and keeping it secret to preserve corporate trade secrets and customer privacy is a growing challenge. A variety of methods and approaches have been introduced and developed for privacy preserving data mining. This paper provides a basic survey of different privacy preserving data mining methods and analyses the representative techniques for privacy preserving data mining.

Method for privacy protection

Abstract: There is provided a method for privacy protection including: identifying an API request being related to a tracking service; generating and sending a predetermined number of initial requests to the tracking service when processing the API requests to the tracking service; storing the initial requests and respective responses related to the initial requests from the API in a database; analysing body objects of the stored initial requests and respective responses and generating a dynamic response recipe on the basis of the analysis; and generating a response including a response body acceptable by the tracking process on the basis of the generated dynamic response recipe.