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The key insight of differential privacy is that as the query is made on the data of fewer and fewer people, more noise needs to be added to the query result to produce the same amount of privacy. Hence the name of the 2006 paper, "Calibrating noise to sensitivity in private data analysis." [citation needed]
Data Flow Analysis: mapping out how the proposed business process handles personal information, identifying clusters of personal information, and creating a diagram of how the personal information flows through the organization as a result of the business activities in question.
Data sanitization is an integral step to privacy preserving data mining because private datasets need to be sanitized before they can be utilized by individuals or companies for analysis. The aim of privacy preserving data mining is to ensure that private information cannot be leaked or accessed by attackers and sensitive data is not traceable ...
Local differential privacy (LDP) is an approach to mitigate the concern of data fusion and analysis techniques used to expose individuals to attacks and disclosures. LDP is a well-known privacy model for distributed architectures that aims to provide privacy guarantees for each user while collecting and analyzing data, protecting from privacy ...
Location data is among the most sensitive data currently being collected. [17] A list of potentially sensitive professional and personal information that could be inferred about an individual knowing only their mobility trace was published in 2009 by the Electronic Frontier Foundation. [18]
Sensitive personal information like the apparent home addresses and health conditions of thousands of active-duty US military personnel can be bought cheaply online from so-called data brokers ...
This technique can also involve adding misleading or distracting data or information so it's harder for an attacker to obtain the needed data. Access to personal data: Here, a user gains control over the privacy of their data within a service because the service provider's infrastructure allows users to inspect, correct or delete all their data ...
A large body of computer science research aims to efficiently and accurately analyze how sensitive personal data (e.g. geolocation, user accounts) flows across the app and when it flows out of the phone. [7] Contextual integrity has been widely referred to when trying to understand the privacy concerns of the objective data flow traces.