Privacy in Real Time Data Release

Consider a scenario where an entity is sharing data at real time to a monitoring for raising an alarm in case something goes wrong. So it makes it necessary to share the data at real-time. However, if the data contains some personally identifiable information which needs to be protected then data owner may be reluctant to share the data even for monitoring.

The dilemma:

Data owning entity is ready to share the data to the monitoring service if it need not share its PII (personally identifiable information)

Technically speaking the goal of the monitoring system is to track patterns and identify exceptional conditions and raise alarm it does not need any personally identifiable information for its task.

Opportunity:

So we as privacy providers need to identify mechanism where data owning entity can share information without PII and monitoring system should be able to perform monitoring with same accuracy.

Solution:

FAST technique: Filtering and adaptive sampling of differentially privacy time series monitoring

FAST is a novel technique for sharing statistical data in real time. FAST uses differential privacy to project user specific data or personally identifiable information and also to ensure that over statistical results are accurate. It adds Laplace noise by sampling the time series into intervals of different size to ensure accuracy of the outcome. The size of sample is varied to by using adaptive sampling technique. Following is the block diagram of the system.

Data stream is fed into the system and it a sample of some default size and adds noise to the sample. Then this perturbed stream is released as output. This stream also goes to the filtering module which predicts the outcome of next stream and identifies the accuracy of the prediction by calculating errors.

The errors are calculated by using a PID controller (Proportional, Integral, derivative). PID controller calculated three errors proportional error, integral error and derivative error. Proportional error is used to keep controller in proportion to current error. Integral error is used to eliminate offset. Derivative error is used to prevent large errors.

Then correction is performed on the size of sample by correction module. Then adaptive sampler modifies the size of sample and takes next sample. This forms a feedback loop to the sampler which is unique contribution of the FAST technique.

Conclusion: There is tremendous opportunity to apply privacy techniques to data that is to be released in real time in various domains.

References:

Liyue Fan and Li Xiong, An Adaptive Approach to Real-Time Aggregate Monitoring With Differential Privacy, IEEE Transactions on Knowledge and Data Engineering, http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6542629