Balancing between Utility and Privacy for k-Anonymity

Organizations need to anonymize the data before releasing them so that data mining cannot predict private information. It's the duty of every organization to ensure privacy of its stakeholders. There is a tradeoff between privacy and utility of the released data. Many methods have been proposed earlier for correlating between the released data and the actual data. All of them use the information theoretic measures. Various methods have been proposed to tackle the privacy preservation problem like Anonymization and perturbation; but the natural consequence of privacy preservation is information loss. The loss of specific information about certain individuals may affect the data quality and in extreme case the data may become completely useless. There are methods like cryptography which completely anonymize the dataset and which renders the dataset useless making the utility of the data is completely lost. One needs to protect the private information and preserve the data utility as much as possible. The objective of this paper is to find an optimum balance between privacy and utility while publishing dataset of any organization. Privacy preservation is hard requirement that must be satisfied and utility is the measure to be optimized. One of the methods for preserving privacy is k-Anonymization which also preserves privacy to a good extent. Many other methods also were proposed after k- Anonymity, but they are impractical. The balancing point will vary from dataset to dataset and the choice of Quasi-identifier sensitive attribute and number of records.