Google Inc.

Alessandro Epasto

Hossein Esfandiari

Vahab Seyed Mirrokni

Sergei Vassilvitskii

Umar Syed

Silvio Lattanzi

Andres Munoz Medina

Seyed Vahab Mirrokni Banadaki

Bryan Perozzi

Alon Altman

Varun Sharma

Stefano Leonardi

Hugh Lynch

Renato Purita Paes Leme

Jonathan Ezra Feldman

A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

Systems and methods for anonymizing large scale datasets

The present disclosure provides a new framework and associated techniques, referred to herein as 'ego-splitting,' that enable the detection of clusters in graphs that are descriptive of networks, including highly complex networks. Ego-splitting leverages local structures within a graph known as ego-nets to de-couple overlapping clusters. For example, an ego-net can be the subgraph induced by the neighborhood of each node. Ego-splitting is a highly scalable and flexible framework, with provable theoretical guarantees. Ego-splitting reduces the complex overlapping clustering problem to a simpler and more amenable non-overlapping (also known as partitioning) problem. Ego-splitting enables the scaling of community detection to graphs with tens of billions of edges and outperforms previous solutions.

Systems and methods to detect clusters in graphs

Example systems and methods enhance user privacy by performing efficient on-device public-private computation on a combination of public and private data, such as, for example, public and private graph data. In particular, the on-device public-private computation framework described herein can enable a device associated with an entity to efficiently compute a combined output that takes into account and is explicitly based upon a combination of data that is associated with the entity and data that is associated with one or more other entities that are private connections of the entity, all without revealing to a centralized computing system a set of locally stored private data that identifies the one or more other entities that are private connections of the entity.

Efficient on-device public-private computation

The present disclosure is directed to encoding graphs. In particular, the methods and systems of the present disclosure can: receive data describing a first graph; and for each node, of one or more nodes, of the first graph, determine, based at least in part on data describing a second graph, and for each of multiple nodes of the second graph corresponding to the node of the first graph, a representation of a role of the node of the multiple nodes in a community to which the node of the multiple nodes belongs.

Methods and systems for encoding graphs

Systems and methods offer an efficient approach to computing similarity rankings in bipartite graphs. An example system includes at least one processor and memory storing a bipartite graph having a first set and a second set of nodes, with nodes in the first set being connected to nodes in the second set by edges. The memory also stores instructions that, when executed by the at least one processor, cause the system to assign each node in the second set to one of a plurality of categories and, for each of the plurality of categories, generate a subgraph. The subgraph comprises of a subset of nodes in the first set and edges linking the nodes in the subset, where the nodes in the subset are selected based on connection to a node in the second set that is assigned to the category. The system uses the subgraph to respond to queries.

Efficient similarity ranking for bipartite graphs

Detecting online attacks is described, including identifying one or more events associated with users on a social graph. For each type of event of the one or more events, generating at least one directed acyclic graph (DAG), where each node on the DAG represents a node on the social graph where an event of the type occurs and each edge on the DAG represents a propagation of the event from a first node of the edge to a second node of the edge.

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New York, NY, United States of America

Alessandro Epasto