Tuesday, September 24, 2019

What is Differential Privacy in iPhone and How Can It Protect Your Data?

Differential Privacy
iPhone Differential Privacy Policy for Data Protection. Differential privacy is a mathematical definition of privacy.

What is Differential Privacy in iPhone and How Can It Protect Your Data?



What is Differential Privacy?

Differential privacy is a mathematical and technical approach for publicly sharing information and BigQuery public datasets, which limits the disclosure of private information recorded in the database. This technique improves the accuracy of queries from statistical databases,  reduces the chances of identifying individual iPhone users and increases the privacy of personal habits and personal data for Apple users.

What is Differential Privacy on iPhone?

Differential privacy is a mathematical definition of privacy originally developed by Nissim, Dwork, Smith, and McSherry with significant contributions from many others over the years.
Apple announced, at the Apple Worldwide Developers Conference (WWDC), a series of new security and privacy features, including one that attracted little attention - and confusion. Specifically, Apple announced that it would use a technology called Differential Privacy (DP) to improve the privacy of its data collection practices.



iPhone Differential Privacy Policy for Data Protection

Starting with iOS 10, Apple uses differential privacy technology to help detect usage patterns, without compromising individual privacy, for a large number of users. Differential privacy adds mathematical noise to a small sample of an individual's pattern of use to hide an individual's identity. As more people share the same pattern, global patterns begin to appear, helping to improve the user experience. In iOS 10, this technology will help improve emoji suggestions, QuickType, Search reports Notes and Spotlight deep link suggestions.

It seems that Apple will collect a lot of data from your phone, to sum up and make a long story. But it does this primarily to make its services better, not to collect the habits of using individual users. Apple has intended to apply advanced statistical techniques to ensure that this aggregate data - the statistical functions you calculate across all your information - does not leak your individual contributions. It sounds very good and seems like at least a good time to talk a little bit about differential privacy, how to achieve it, and what it might mean for your Apple iPhone.

In the past few years, some people have been used to the idea that they send a lot of personal information to many of the service providers for services they use in their Phones. A number of surveys indicate that average people are beginning to feel uncomfortable about information privacy and information security. This discomfort makes sense when you consider that companies may use your personal data for marketing. But you should always remember that sometimes there are decent motives for collecting user information.
For example, Google famously turns on Google Flu Trends. Microsoft recently announced a tool that can diagnose pancreatic cancer by monitoring Bing queries. Of course, you benefit from data from group sources that improve the quality of your services - from map apps to restaurant reviews. Unfortunately, data collection is often based on good intentions but may become worse.
For example, in late 2000, Netflix conducted a competition to develop a better algorithm to recommend films. Netflix released an “anonymous” demo dataset to lead the competition that was stripped of identifying information. Unfortunately, this process of de-identification has proved inadequate.

In a famous work, Narayanan and Shmatikov showed that if you know a little additional information about a particular user, the datasets can be used to redefine specific users - For example, - predict their political affiliation!
This kind of concern should be worrying to us. Not only because companies routinely share data but because statistics about the dataset can sometimes leak information about the individual records used in their calculation and because hacks always occur. Differential privacy is a set of tools that were designed to identify and solve this types of problems.



Characteristics of a Differential Privacy

The characteristics of differential privacy can intuitively summarize as follows:

The main characteristics of differential privacy research is that in many cases, differential privacy can be realized if the data sorting party is ready to add random noise to the result. For example, instead of merely collecting and sorting real data, noise can be introduced from the Laplace or Gaussian distribution, which produces a completely inaccurate result - but it presents the contents of any given row.

Imagine that you have two identical databases, one containing your information, and the other without. Differential privacy ensures that the probability of any statistical the operation will be (almost) the same as whether it is performed on the first or second database.
Even differential privacy is more useful, pump noise can be calculated without knowing the contents of the database. Noise calculation can be performed based on the knowledge of the function to be calculated and the acceptable amount of data leakage.

One way to look at this is that differential privacy provides a way to see if your data has a significant impact on query results. If not, you may also contribute to the database - since there is almost no damage. Let's take an example:
Imagine you have chosen to enable the reporting feature on your iPhone which tells Apple if you want to routinely use emoji in your iMessage conversations. This report indicates such information: 1 indicates you like, and 0 indicates you do not like. Apple may receive these reports and fill them in a huge database. At the end of the day, it wants to be able to derive the number of users who love these special emojis.

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