Many applications inherently disclose information because perfect privacy protection is prohibitively expensive. RFID tags, for example, cannot be equipped with the cryptographic p...
Privacy-preserving data mining has concentrated on obtaining valid results when the input data is private. An extreme example is Secure Multiparty Computation-based methods, where...
We propose a software framework that augments context data with a range of assorted confidence/reputation metadata for dimensions such as security, privacy, safety, reliability, or...
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Data protection legislation was originally defined for a context where personal information is mostly stored on centralized servers with limited connectivity or openness to 3rd pa...