Verification of privacy in a shared resource environment

A computer program product comprising:

• a computer readable storage medium having program code executable by a processor to automatically perform a privacy preservation assessment against a shared pool of configurable computing resources and associated data, the assessment including de-anonymization of an associated dataset and inferring private information informative as to an identity of an individual associated with the dataset, the program code executable by the processor to:
• receive a preferred level of privacy for service and data associated with a computing resource;
• individually subject a plurality of individual candidate inferences to a confidence level assessment to assess, for each of the individual candidate inferences, a confidence level associated with the individual candidate inference;
• form a set of inferred entities, including selectively assign or not assign each of the individual candidate inferences to the set of inferred entities responsive to the confidence level associated with the individual candidate inference;
• perform the privacy preservation assessment for the formed set of inferred entities, including the performed assessment to return a privacy score for the formed set, the score being a leakage indicator of private information associated with the service;
• and
• selectively populate a data container with the formed set of inferred entities violating the preferred privacy level.

A computer-implemented method comprising:

• automatically performing a privacy preservation assessment against a shared pool of configurable computing resources and associated data, the assessment including de-anonymizing an associated dataset and inferring private information informative as to an identity of an individual associated with the dataset, including:
• receiving a preferred level of privacy for service and data associated with computing resources;
• individually subjecting a plurality of individual candidate inferences to a confidence level assessment to assess, for each of the individual candidate inferences, a confidence level associated with the individual candidate inference;
• forming a set of inferred entities, including selectively assigning or not assigning each of the individual candidate inferences to the set of inferred entities responsive to the confidence level associated with the individual candidate inference;
• and
• performing the privacy preservation assessment for the formed set of inferred entities, including returning a privacy score for the formed set, the score being a leakage indicator of private information associated with the service;
• and
• selectively populating a data container with the formed set of inferred entities responsive to the returned privacy violating the privacy expectation level.