Bigid Inc.

Eyal Sacharov

Nimrod Vax

Yehoshua Enuka

Itamar Apel

Dimitri Sirota

David Moyal

Meghan Mergui

The disclosed privacy management platforms are adapted to scan any number of data sources in order to provide users with visibility into stored personal information, risk associated with storing such information and/or usage activity relating to such information. The platforms may correlate personal information findings to specific data subjects and may employ machine learning models to classify findings as corresponding to a particular personal information attribute to provide an indexed inventory across multiple data sources.

Machine learning system and methods for determining confidence levels of personal information findings

Exemplary privacy management platforms are described herein. Such platforms may be embodied in systems, computer-implemented methods, apparatuses and/or software applications. The described privacy management platform may be configured to scan identity, primary and/or secondary data sources in order to provide users with visibility into stored personal information, risk associated with storing such information and usage activity relating to such information. The platform may correlate personal information to specific data subjects to provide an indexed inventory across multiple data sources.

System and methods for privacy management

Systems, methods and apparatuses are disclosed to cluster a plurality of documents located in any number of local and/or remote systems and applications. Preprocessed text is generated for each document, and a hash and a feature vector are determined based on the preprocessed text. A set of clusters is retrieved, wherein each cluster is associated with a hash list and a cumulative feature vector. Each of the documents may then be associated with a cluster by comparing the hash of the document to the hash lists of the clusters and/or by determining similarities between the feature vector of the document and the cumulative feature vectors of the clusters.

Dynamic document clustering and keyword extraction

The presently disclosed subject matter includes a computerized method and system of clustering data-subsets of one or more structured or semi-structured data sources, each data-subset comprising data-values. For each data-subset (e.g. column) in a group of data-subsets extracted from the one or more structured or semi-structured data sources, a respective vector indicative of characters' position distribution in data-values in the data-subset is generated, thereby giving rise to a group of vectors; for each vector in the group of vectors, a respective proxy hash value is calculated, and data-subsets of respective proxy hash values are assigned to clusters according to similarity between the respective proxy hash values and between the respective vectors.

Clustering of structured and semi-structured data

Systems, methods and apparatuses are disclosed to efficiently and accurately scan a plurality of documents located in any number of unstructured data sources. Preprocessed metadata is generated for each document and metadata features are determined based on the preprocessed metadata. A trained machine learning system may utilize the metadata features to predict whether each of the documents contains personal information, without requiring any information relating to the content of such documents.

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Herzliya, Israel

Eyal Sacharov