Microsoft Technology Licensing, LLC

Samuel Terrence Trim

Xiaoming Wang

Stephen White

Priyatham Reddy Allala

Michael Gordon Hale

Nagarjuna Aravapalli

Neeta Vinod Atal

Praneeth Reddy Vatti

Jason Drew Black

Eliyohu Aron Inger

Yan Guo

Keying Li

Patricia Anne Donnellan

Senthil Vel Gunasekaran

Aby John

Nicholas Gregg Brown

Yuxiang Xu

Brad Snow

Kimberly Denise Lucy

Aaron J Moreau-Cook

This document relates to evaluating privacy impact for organization risk management. For example, quantifiable methods are provided by way of a privacy impact model to calculate a relative value for privacy impact can be used to calculate risk and prioritize risk mitigations and take corrective actions.

Quantifying privacy impact

It can be difficult to manage assets, particularly when determining ownership of assets. Systems and methods for facilitating identification of ownership of an asset include identifying an asset (e.g., an item capable of being owned), identifying one or more actors (e.g., an entity capable of owning an asset), and identifying interactions between the asset and each actor. The systems and methods additionally apply a decay factor to the identified interactions to cause a reduction in the significance of the identified interactions between the asset and each actor and produce an asset ownership score for each actor based on the decay-modified interactions. The resulting asset ownership score for each actor is provided to an entity in a fashion that allows the entity to identify a comparative likelihood that each actor is a potential owner of the asset.

Method of discovering and modeling actor and asset relationships across a cloud ecosystem

Displaying a representation of an asset. The method includes an act of displaying a different representation for at least a first actor of the one or more actors. An asset being an item capable of being owned, and actors being entities capable of owning an asset. The method also includes displaying the representation of the asset and displaying the different representation for the first actor such that a likelihood of the first actor owning the asset can be evaluated based on the representation and the different representation.

Modeling actor and asset relationships

A list of electronic mail (e-mail) accounts is extracted from an electronic mail system. A list of electronic mail accounts, with forwarding enabled, are identified as a set of collection accounts. A dropbox account is identified, from the collection accounts, as a destination e-mail account for the forwarded collection accounts. The collection accounts that forward to the dropbox account that has in excess of a threshold number of collection accounts forwarding to it, are identified as malicious e-mail collection accounts and are forwarded to a resolution system, for resolution.

Identifying user behavior in a distributed computing system

Various embodiments pertain to techniques for predicting and isolating patterns or trends across datasets. In various embodiments, one or more Q-entities are extracted from a data seed, associated with one or more dimensions, and classified into one or more clusters for each dimension with which it is associated. In some embodiments, a Q-entity can exist in more than one dimension and/or more than one cluster within a dimension. Once information from the data seed is associated with a dimension and cluster, frequency analysis can be utilized to ascertain a pattern or trend in the data. In various embodiments, additional data can be processed, added to the dimensions and clusters, and frequency analysis can be performed on the updated dataset to provide additional information on the pattern or trend.

Prediction and isolation of patterns across datasets

Growing community of inventors

Sammamish, WA, United States of America

Samuel Terrence Trim