The Prior Art Database and Publishing service will be updated on Sunday, February 25th, from 1-3pm ET. You may experience brief service interruptions during that time.
Browse Prior Art Database

System, Method and Apparatus for Optimizing Privacy Enforcement via Interaction Analysis

IP.com Disclosure Number: IPCOM000248189D
Publication Date: 2016-Nov-07
Document File: 2 page(s) / 46K

Publishing Venue

The IP.com Prior Art Database


Efficient privacy enforcement where based on offline analysis of a multiplicity of applications, statistical correlations (e.g. computed via log-linear analysis) are determined between releases of private fields; these correlations then govern the synthesis of a runtime tracking policy; and finally, the tracking scheme (e.g. taint analysis) is applied at runtime to a subject application via code instrumentation

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 53% of the total text.

Page 01 of 2

System, ,

Method and Apparatus for Optimizing Privacy Enforcement via Interaction

      Method and Apparatus for Optimizing Privacy Enforcement via Interaction Analysis



Mobile devices often make access to, and release, private user information (e.g., the user's SIM/device ID, location, contacts, etc)

While some use cases are justified (e.g., authentication or location-based services), there are also less justified scenarios (e.g., contextual advertising or analytics that are sometimes overly intrusive) Privacy Enforcement


Privacy enforcement is a collective term that captures runtime techniques to detect, and mitigate, privacy threats

Several such systems are available:

TaintDroid [Enck]

MockDroid [Beresford]

AppFence [Hornyack]



Performance overhead:

Privacy enforcement involves runtime tracking of private fields and transformations thereof (often in the form of taint tracking)

There is inherent overhead in propagating tracking labels Memory footprint:

Fine-grained tracking necessitates per-object labels

The memory cost of these labels can become significant [Bell]

Our Solution: Outline


Find out the connections between different private fields (e.g., age and gender)

Adapt the enforcement system according to the uncovered association rules

If fields A and B are strongly related, then we can track them as a single unit

If fields A and B are inversely related, then once A is released we can coarsen tracking of B (and vice versa)

Large-scale analysis of 1,462 Apps from 25 different c...