DIFFERENTIAL PRIVACY IN BIG DATA ANALYTICS FOR HAPTIC APPLICATIONS

Preservation of privacy in big data has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation and publishing. Many new techniques have been suggested and implemented for privacy preservation in big data but unfortunately they seems to be failing due to the nature of big data, previous methods and traditional methods like k-anonymity and other anonymization techniques have overlooked privacy protection issues leading to privacy infringement. In this work, a differential privacy protection scheme for ‘big data in body area network' is developed. Compared with previous methods, the proposed privacy protection scheme is best in terms of availability and reliability. “Differential Privacy” (DP) prevents unwanted re-identification and other privacy threats to individuals whose personal information is present in large datasets, while providing useful access to data. Under the DP model, personal information (micro data) in a large database is not modified and released for analysts to use. Differential privacy works by inserting an intermediary piece of software between the analyst and the database. The analyst never gets to access or actually see the contents of the database; instead the intermediary acts as a privacy-protecting screen or filter, effectively serving as a privacy guard. To verify the advantages of our scheme, several experiments are conducted to show the results. Exploratory results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy. Body Area Networks (BANs), collects enormous data by wearable sensors which contains sensitive information such as physical condition, location information, and so on, which needs protection.