# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 6 Evaluation
### Resource Conservation  
Wasting of resources is a typical problem of opportunistic networking schemes as multiple copies of the same data exist across the network. Consider a simple example where an Agent receives three different encrypted requests from Requesters R1, R2 and R3 and all of them are for the same advert. When visiting the Ad-Dealer, the Agent would need to collect three copies of the same advert, each copy encrypted with a different key for each Requester. This increases overhead on the Agent side who needs to download and store excess data.  
Our system reduces this overhead by taking advantage of the multicast nature of advert delivery as the same advert is relevant for multiple consumers. Instead of circulating multiple copies of the same advert to different users, we are able to spread a single copy that is addressed to multiple recipients. What is innovative about our design is that we achieve this without compromising privacy, as we have already demonstrated in previous sections.  
Interest commonalities are expected to emerge naturally among social groups who share the same demographics (age, religion, nationality, etc.) [19]. To further enhance this effect, our system attempts to identify specific shared interests by comparing the user's UIPs. Although it may be counter-intuitive, our aim here is not to achieve complete accuracy. A completely accurate discovery of common interests could compromise privacy among Agents and Requesters. The probabilistic approach that we follow preserves privacy, but at the same time, increases the chances of a randomly selected interest being shared by two users.  
This is better illustrated in a simple example. Consider a scenario where a potential Requester wishes to place a request to an Agent. The Agent's interest profile UIP consists of 30 entries out of a total of 100 interests that are supported by the system. This means that if the requester were to perform a random selection, he/she would have a 30% chance of selecting one of the Agent's interests. When we apply our algorithm, the profile is segmented into five groups of twenty interests. The groups are then ranked into descending order which the highest ranked containing 15 interests, the second highest 10, the third five and the two remaining groups containing zero. This enables the Requester to effectively focus his/her priorities on the higher ranked groups and ignore the rest. This would give him a 75% chance of success for the top ranked group, 50% for the second highest ranked group and 25% for the third.  
Naturally, these numbers will vary depending on how sparsely the Agent's interests are spread across the UIP. The total number and size of the groups can be altered depending on the number of supported interests and the desired level of accuracy. In addition to this, the groups can potentially be composed based on logical criteria, e.g., each group containing interests that are relevant to specific types of consumers. For example, a group may include interests that are commonly associated with male consumers while another group will include interests that are common among females. This is very similar to the profiling method that is currently used by advertising companies and will result in greater accumulation of marked entries within specific groups. However, in order to maintain privacy,the groups need to be created based on criteria that do not expose any information that the Requester and Agent do not already know about each other. This may include demographic information that is very generic such as gender, age and nationality.