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EdRingtoneBig collection of android apps, ringtones, mobile themes, hd wallpapers, games for phone and tablet. Billions of free downloads served. Play and listen latest popular music mp3 with a good quality online streaming. You can find more than a million free mp3 for downloads. Treat the fam to 1 free month of YouTube Red. Adfree music for up to 6 household accounts. You might think of Uber as a ridehailing company or a lawsuitridden selfdriving car developer, but at its core, Uber is a big data company. It has to constantly. Live Share Grundig. Uber Unveils New Privacy Tool That Protects Individual User Data. You might think of Uber as a ride hailing company or a lawsuit ridden self driving car developer, but at its core, Uber is a big data company. It has to constantly crunch location coordinates, traffic data, payment information, and tax ratesand putting all that data in Ubers hands sometimes makes users nervous. But now Uber is debuting a differential privacy tool that it will use to analyze its vast data stores. Razzle Magazine June 2013. S 800 Characters Mugen. Differential privacy allows for analysis of large data sets without revealing the identity of any individual included in the data, and is used by companies like Apple and Google to gain insight from user data without compromising privacy. Ubers new tool will let its data analysts know the likely privacy implications of any queries they make on Ubers data before they make them. Effectively, its a way to take a look at queries and decide how sensitive the resulting data is from that query without having to run the query, Ubers manager of privacy engineering Menotti Minutillo told Gizmodo. Heres how itll work Imagine Uber data analysts want to figure out what the average distance is for a ride in San Francisco. Theyll need to query large swathes of data about rides in the city, but pulling that thread could expose lots of information about individual riders and drivers. Differential privacy scrambles the data and injects noise, making it impossible to trace trip information back to a particular user. But some queries are more sensitive than others, and therefore require more noise. The average trip distance in a smaller city with far fewer trips is more influenced by a single trip and may require more noise to provide the same degree of privacy. Differential privacy defines the precise amount of noise required given the sensitivity, Katie Tezapsidis, an Uber software engineer on the privacy team, explained in a blog post announcing the change. In order to calculate that sensitivity, Uber partnered with a team of security researchers from the University of California, Berkeley. The researchers worked for over a year to come up with the calculation technique, nicknamed Elastic Sensitivity, which Uber is releasing today as an open source tool. Elastic Sensitivity will make it possible for analysts at Uberand elsewhereto quickly adapt differential privacy standards to a variety of queries. Previously, an analyst would have queried a database and then tried to weed out sensitive or unnecessary data after the fact. Now, data will come out clean. Our team is very, very interested in providing the tools and platforms so people can do their job in a privacy appropriate way, Minutillo said. The tool will be able to make suggestions about how much noise should be added in order to preserve privacy, or whether the query should be run at all. In cases where you have a legitimate useyou need to retrieve data to do analysisthis is an additional layer of protection, Minutillo added. We can feel comfortable that the analyst can still get results that are correct, and reduce the risk of singling out any individual thats in that set. Ed Ringtone' title='1 Ed Ringtone' />
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