Ioannis Chrysakis: The CAP-A Best Practice: Towards Enabling a Privacy-Friendly Apps Market by Applying a Crowdsourcing-Based Evaluation Approach

Thank you very much once again. So this is the agenda of my talk for today. A brief introduction to the big data evolution and the, the application data usage. And then I will present you the main problem and the respective challenges regarding to this problem. The third item in the agenda is the main solution. What we actually propose as part of the cafe project is and implemented cap tools. And finally our general vision, why we can see that cross crossroad application application can benefit both service providers and users. So for the next one minute, let's talk about data. Have you ever thought how many data are being transmitted in a single minute? For example, you can, if you can focus on application, you can see that 400,000 applications are being downloaded by users. There are many, many data that are being produced by users that use digital services, applications, and things like that.
And of course, in this data, there's, there are a lot of data that are considered as personal data. So let's see the market perspective here. We can see that there is an increasing internet users for mobile users and as such, we have a growing market in the apps in the application market. So the predictions for 2022 say that we could have a 258 billion applications available for consumers. If you can, if can see the user's perspective here, you can see that users dedicate spent a lot of time by downloaded, by installing, by using actually these applications. And on average, for example, recent study says that person has installed 80 apps on their phone and users usually use their phone about 30 times a day. So we can see that in many countries, users spent a lot of time in their phones, which is, let's say more than the time that dedicate to watch TV is very interesting to see.
So let's see the problem here. On one hand, we have the applications and the other hand, they are the reasons so applications, firstly, application request access to a set of personal data through device permissions. Secondly, the privacy policy that describe how this data handle the problem with these documents is that are really lengthy. They contain legal terms, they have frequent updates, so it is difficult to read them. It's difficult to follow these changes. And thirdly applications use our personal data for several purposes from our advertising user identification, even vote manipulation. So this application can enable data sharing with the third parties. And this is a big, big problem for, for the users. On the other hand, users accept all these requests by which could have made by applications users. Don't devote time to privacy policy. This is true. And at the end users don't know about the personal data handling.
So finally they cannot contribute, contribute to the problem. They cannot improve the situation, even if there is also establish legal framework, such as GT P R. So let's see the solution here. First of all, this solution, as I said before, is a crowdsource based solution. It is based on the crowd is based on the users. So the first thing that I will present you is the cafe tools. So through the cap tools, users can participate in, in an evaluation of the applications. The second thing is that the results of this evaluation have been aggregated under two basic metrics. And the third thing here is that we apply a reward mechanism to enable user engagement and to provide gamification features to the users and maximize this way, the final contribution. So let me present you the, the main tools of cafe, the cap Porwal is a tool that supports several crowdsourcing activities for evaluating the privacy behavior of applications.
I can, I, I will present you three main actions that cafe Porwal in includes, for example, through cafe Porwal users can add expectations on application permissions and, and can express if they agree or disagree with a specific permission to a specific app. For example, also they can share evidence links for articles related to privacy for specific cap, of course, and they can also add the notation of privacy policy documents, and they can relate specific paragraph of, of this notation with GTP or related legislation aspects as they can. They have been visualized and you can see how they can be expressed in the Porwal. The one, one also one thing that I can also add here is the statistics part. The cafe Porwal offers meaningful statistics by providing visual presentations of aggregated formation. For example, cafe Porwal offers leader with top to end of applications with several criteria or talk to any of users according to their contribution.
Then the interesting thing here is that we can identify patterns. We can call them privacy norm, which are actually specific preferences of users to specific apps categories. For example, parents are more concerned about applications that are downloaded by their kids, for example, game applications. And finally developers understand how close their services are by using a Porwal to cut that, to cut clients actually wish finally, policy makers can identify specific trends through a new market applications. The second tool is the cap app. It's a native application. We can find it in play store. You can additionally audit your install application to cap app. And of course there's a there's interaction with cafe Porwal. You can also express privacy expectation and you can also see explore specific privacy information regarding application that have been already crawled in the cap. Porwal. So, as I said before, something which is really important to this work is the campaign metrics.
It, it is the, the calculation of, of evaluation metrics actually includes all user input in this, in this metrics. So the first metric is the satisfaction of community expectations score the satisfaction of community measures. How close is the privacy, how close the privacy expectation regarding the applications are to cut the app is actually requesting. And then second metric called the privacy score takes, takes into account privacy related aspects, such as how easy to understand privacy policy documents, how to associate annotation specific DPR concepts. So these are the two specific scores that embraces all the results in a graphical way. And this is one of the graphical presentation of the scores through specific icons and the fi last but not list. The cap me give us a combination of intrinsic and tracing reward by giving points to users that participate in specific tasks by enable user tier by enable gamification features. And one interesting thing here is to, to report is that the cap mechanism is based on a flexible concept model actually is implement ontology called record ontology. So we can adapt this to different point system to different tasks and is very flexible for us to, to enable a new recording scheme in the cafe. Porwal.
So by closing my presentation, I will give you our general vision to the problem, how we can enable the privacy friendly app market. So users use cap tools to evaluate the mobile labs behavior in terms of privacy developers improve their applications based on user evaluation. And finally service providers can identify trends through the statistics and through this course, and finally they can invest in new application or provide improvements for existing products. So the bottom line here is how to view privacy as a business innovation value as a new value that that can, that can be added to a new privacy friendly market of applications. So this is the, the cap team in fourth cap team consists of members of four to computer site for proxy and in two company based in Germany, if you have time, I strongly recommend to, to, to watch these two videos about five minutes maximum.
So the first video describes the cap tools and the second video describes the idea of the, of the formulated community called capric community. So here are some references, the capric community, the community that runs and actually supports this project, the Cappe project and NGI trust project, which actually funds this, this kind of tools that I presented before some related publications. And before closing my presentation, I would like to invite you to register to the Porwal, download the cafe app and make your own private expectations. And it'll be more than happy for me to answer any question regarding this work. So I would like to thank you and I'm really open to any question.