In few hours I will present MobiRate. Fortunately, the slides are ready ! See them next. A short description follows.
P.S. I’ll blog about Ubicomp shortly. For now, look at the great coverage by Albrecht Schmidt
Q&A Session (at the conference):
Q> You have shown that MobiRate effectively protects against *indepedent* malicious individuals. What if malicious individuals collude?
A> Colluding malicious peole will not be able to tweak ratings because they cannot produce fake crypto material. However, if malicious people collude, one may well run into updating problems. Phones update their ratings while they move and, consequently, there are time windows in which ratings are not up-to-date. During those time-windows, colluding people may succeed in attacking the communities they are in (e.g., in flooding the system with spam content).
Q> Phones that run MobiRate audit each other. Are their users aware of that?
A> We have assumed that, in downloading and running MobiRate, people silently agree with the possibility of their phones being “auditors”. However, people should be able to step back and refuse to be auditors at times; for example, whenever they are running out of battery. This feature should be definetely
included in the next version of MobiRate.
Q> Your solution is general, in that, it is able to collect and store not only user ratings but also user activities!
True. Instead of monitoring ratings, one could force people in keeping a record of their activities. Before deploying MobiRate, we should carefully think about its misuses and try to prevent them. A good starting point could be to understand how “historical misuses of technology can be studied to be avoided in the future” (link)
Short Description of MobiRate:
- Situation: To share services, mobile devices may need to locate reputable in-range providers and, to do so, they may exchange ratings with each other.
- Complication: Providers may well tweak ratings to their own advantage.
- Proposal: We have designed a new decentralized mechanism (dubbed MobiRate) with which mobile devices store ratings in (local) tamper-evident tables and check the integrity of those tables through a gossiping protocol. We evaluate the extent to which MobiRate reduces the impact of tampered ratings and consequently locates reputable service providers. We do so using real mobility and social network data. We also assess computational and communication costs of MobiRate on mobile phones.