Context-based Detection of Privacy Violation

1. Context-based Detection of Privacy Violation Bharat Bhargava In collaboration with Mark Linderman (AFRL) and Chao Wang (UNCC) Purdue University bbshail@purdue.edu

2. Private Data Released by Trusted Parties • Cloud or SoA environments • Data Dissemination or Warehouses • Trading Trust for Privacy • Obligations may not be enforced • Data and policy get separated

3. Types of Privacy Violations • Open violation and release of private data • Slow release or inadvertent release • Opportunistic release of data for gain • Owner may benefit or see adverse effects • Privacy policies may not be clear • The owner is interested in the origin, timing, content, and extent of privacy violation so as to protect herself for damage assessment and control.

4. How to detect Privacy Violations? • Get feedback on the affects of privacy disclosure. • Acceleration of the access to the private object by users other than violators • Objects in the sphere of control of the private object may see abnormal activity • The sphere of control can be determined based on dependencies, concurrent activities and objects the transaction are reading and writing • These fuzzy observations can be used to identify the violation of privacy of an object

5. Diffusing the Affects of Privacy Violation • The owner of private data can release more objects for violators and other users. This increase in entropy may take the attention away from sensitive objects • If the owner can determine the origin of violators, it can also take revenge by releasing their private data by running transactions that involve data from owner and violator and thus control the behavior( Controversial)