Wavelet Transforms Through Differential Privacy

Provided by: IJCER
Topic: Security
Format: PDF
Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. However, many solutions exist for privacy preserving data; differential privacy has emerged as a new paradigm for privacy protection with very conservative assumptions and guarantees the strongest privacy. In particular, for a count query answered by output data set, the noise in the result makes it vain as the result set could be equivalent to the number of tuples in the data.

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