A privacy-preserving framework for managing mobile ad requests and billing information

Abstract

Organizations are starting to realize the significant value of advertising on mobile devices, and a number of systems have been developed to exploit this opportunity. From a privacy perspective, practically all systems developed so far are based either on a trusted third-party model or on a generalized architecture. We propose a system for delivering context, location, time, and preference-aware advertisements to mobiles with a novel architecture to preserve privacy.  The main adversary in our model is the server distributing the ads, which is trying to identify users and track them, and to a lesser extent, other peers in the wireless  network. When a node is interested in an ad, it forms a group of nearby nodes seeking ads and willing to cooperate to achieve privacy. Peers combine their interests using a shuffling mechanism in an ad-hoc network and send them through a primary peer to the ad-server. In this way, preferences are masqueraded to request custom ads, which are then distributed by the primary peer. Another mechanism is proposed to implement the billing process without disclosing user identities.

Aim

The aim is to provide a system for delivering context, location, time, and reference-aware advertisements to mobiles with a novel architecture to preserve privacy.

Scope

The scope is to implement the billing process without disclosing user identities, preferences are masqueraded to request custom ads, which are then distributed by the primary peer.

Existing System

Whether it is TV, radio, newspapers, or the Internet, advertisements generate substantial revenues. According to the Interactive Advertising Bureau (IAB), $8.4 Billion is the U.S. Internet advertising revenue in the first quarter of 2012. And, as mobile devices get more involved as media delivery platforms, the worth of advertising on these devices becomes significant. With billions of mobile users worldwide, it is indeed a potentially huge market for advertising. Moreover, considering that a decent fraction of these users own smart phones or tablets certainly expands this opportunity. These users spend significant time browsing the different multimedia and gaming capabilities of their devices, making them more exposed to ads. Also, these devices now come with Wi‑Fi and 3G, meaning they can be reached virtually everywhere. Add to this GPS capability and computing user preferences, and a new level of targeted advertising can be attained. Personalized ads that can match users’ preferences with products and services in their vicinities have much higher chances of succeeding in capturing these users’ attention and achieving better customer satisfaction, consequently increasing the profitability of ads.

From a privacy perspective, practically all systems developed so far are based either on a trusted third-party model or on a generalized architecture.

Disadvantages

·      Most of the developed systems do not take privacy into consideration. Even in MobiAd, which considers privacy, there is no clear information on the implementation details and the billing process.

·      The same aspects that make these devices great platforms for advertising also impose strict guidelines since they contain key private data, like contacts information and calendar entries. Hence, proper use and confidentiality of this data should be respected.

 Proposed System

Mobile advertising relies on content providers like applications and web pages to deliver ads to users. Service providers register ads to an ad server, which delivers them to users through content providers who usually subscribe to host ads for profit making. When a user accesses an application subscribed to an ad server, the application requests an ad from the server with the user location and id. The server then checks based on the id the interests of the user through an online profile, and delivers targeted ads that refer to service providers in the vicinity of the user which are relevant to his interests. For example, a user in downtown San Francisco interested in pizza will get an ad for pizzerias within that location. After the user clicks the delivered ad, a click report is sent to the ad server for billing purposes. In this scenario we can make the following observations about the ad server. It has the ability to track users even if it declares that such information is not being stored, knowing that most ad providers acknowledge that they store such information. The ad server has access to all the users'ʹ personal information including their interests and location info, and thus it can easily profile users. Even though such privacy invasion of user personal information is currently regulated by the industry, several users decide not to opt-in because of privacy concerns. Thus, implementing a privacy preserving architecture will ensure a greater extent of penetration of targeted advertising.

Advantages

This project addresses this gap by integrating privacy preservation into the design.

1) A simpler overall system that allows peers to send requests any time, and does not require them to back off for a certain time after becoming primary.

2) An extended fairness method that makes fairness in the system long lived, i.e., widespread among all peers, and not temporary where it only applies to peers in the same group.

3) A new billing system that does not rely on a trusted third party model (server).

 4) A more robust shuffling technique with multiple encryption levels that greatly increases privacy.

5) An aggregation scheme that retains privacy even when the number of peers in the group is small.

6) A new caching system that stores not only application requests but also specific requested ads.

 7) Algorithms that make the system more resilient to malicious attempts to breach user privacy.

8) A thorough overhead analysis of request delay, generated traffic, and battery energy consumption.

9) A simulation platform for testing system privacy while varying various system parameters.

System Architecture

Overall system components

 

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                Processor          -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board         -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System       : Windows  7                                    

·                Front End                  : JSP AND SERVLET

·                Database                  : MYSQL

·                Tool                           :NETBEANS

References

Hassan Artail, and Raja Farhat “A PRIVACY-PRESERVING FRAMEWORK FOR MANAGING MOBILE AD REQUESTS AND BILLING INFORMATION”

Mobile Computing, IEEE Transactions on  (Volume:14 ,  Issue: 8 ) September 2014