Spatial Bloom Filters

The evolution of mobile devices and the diffusion of location-based services (LBS), establish a cornerstone in the digital era. Traditional information (as names, addresses and phone numbers) running across the internet and through a number of other services are now coupled with positional data. With such an impressive amount of information, service providers are able to determine precise location estimates and to know almost everything concerning our opinions, religious and political preferences and private life in general. Omitting the most obvious concerns this data leakage leads to, people should focus on manipulation and threats to autonomy and freedom itself. When our society knows everything about us, our daily life may be conditioned by our position, becoming factitious and unnatural. Anonymous and secure location-based services are thus an important challenge we need to face, with the aim of saving the benefit while avoiding risks and personal debasement.

Beyond location data

Even though SBF were initially designed to handle location-based information, they are suitable for a number of different scenarios.

Recently, this data structure was proposed in the field of secure and private routing. Moreover, due to its set based nature, we believe it should be a promising technique whenever the membership of some object among several sets is to be evaluated in a secure and privacy-preserving context. SBF should for instance behave well in a lot of pattern recognition problems, especially when they deal with sensitive data (as biometric ones).