Microsoft Technology Licensing, LLC

Raghav Kaushik

Arvind Arasu

Donald Kossmann

Kenneth Hiroshi Eguro

Panagiotis Antonopoulos

Ravishankar Ramamurthy

Ravi Ramamurthy

Dilli Dorai Minnal Arumugam

Steven Richard Gott

Manas Rajendra Joglekar

Aditya Nigam

Nitish Gupta

Yueren Wang

Nikolas Christopher Ogg

Pankaj Prabhakar Naik

Elnata Degefa

Andreas Wolter

Jakub J Szymaszek

Vineet Pandey

Pingfan Meng

Nikhil Vithlani

Surajit J Chaudhuri

Vivek Ravindranath Narasayya

Kris Kuppuswamy Ganjam

Cesar Alejandro Galindo-Legaria

Conor J Cunningham

Joachim Hammer

Michael I Zwilling

Bala Neerumalla

Rakesh Khanduja

Shafi Ahmad

Kedar Dubhashi

Donald Alan Kossmann

Methods for centralized access control for cloud relational database management system resources are performed by systems and devices. The methods utilize a central policy storage, managed externally to database servers, which stores external policies for access to internal database resources at up to fine granularity. Database servers in the processing system each receive external access policies that correspond to users of the system by push or pull operations from the central policy storage, and store the external access policies in a cache of the database servers for databases. For resource access, access conditions are determined via policy engines of database servers based on an external access policy in the cache that corresponds to a user, responsive to a resource access request from a device of the user specifying the internal resource. Data associated with the resource is provided to the user based on the access condition being met.

Centralized access control for cloud relational database management system resources

Deferred verification of the integrity of data operations over a set of data that is hosted at an untrusted module (UM) is controlled. The controlling includes generating a request for a data operation on the set of data. The request includes an authentication portion. The request is sent to the UM. A response to the request is received from the UM. The response includes cryptographic verification information attesting the integrity of the data operation with respect to prior data operations on the set of data. The response includes results from deferred verification at a trusted module (TM).

Controlling verification of key-value stores

Methods for asynchronously determining relational data integrity using cryptographic data structures are performed by systems and devices. Changes in current tables of relational databases are reflected in associated history tables. Cryptographic hybrid blockchain ledgers are updated with transaction records, for entry changes in current and history tables, including transaction information and hash values of corresponding entry changes. Hybrid blockchain ledgers also include root hash values of Merkle trees of transaction records in current blocks, and hash values of prior blocks. A current block receipt is asynchronously generated and provided as a single hash value from which the validity states of the tables and ledger are able to be verified. Cryptographic receipts of specific transactions reflected in table entry changes are generated and provide immutable evidence of specific transaction existence for users. Ledger-enabled tables are provided for mixed database operations with ledger-disabled tables, and temporal history table database operations are enabled.

Asynchronously determining relational data integrity using cryptographic data structures

A 'Database Confidentiality System' provides various techniques for using server-side trusted computing in combination with configurable type metadata and user- or system-definable rules associated with individual database fields to implement database confidentiality. In various implementations, type metadata and one or more rules are added to each database field. Metadata includes a domain, method of encryption, and a pointer to an encryption key used to encrypt the data in the corresponding field. The rules define one or more operations allowed on the corresponding data types. The type metadata and rules are optionally integrity protected and/or encrypted to avoid unauthorized changes or access. Various encryption techniques (e.g., probabilistic, Paillier, etc.) allow some computations to be performed in an untrusted environment without access to the encryption key. This enables the Database Confidentiality System to maintain database confidentiality while performing distributed computation and communications between the untrusted machine and the trusted machine.

Type-based database confidentiality using trusted computing

Computer systems, devices, and associated methods of evaluating an expression comprising restricted data are disclosed herein. In one embodiment, a method includes receiving a database statement from a client application and verifying the authenticity of the database statement. If the database statement is authentic, an approved expression is identified in the database statement for creating an evaluation rule. The method further includes restricting evaluation of expressions in a protected computing environment according to the created evaluation rule.

Expression evaluation of database statements for restricted data

Computer systems, devices, and associated methods of optimizing the execution of instructions of a database statement by a database server are disclosed herein. In one embodiment, a method includes identifying a potential execution plan for executing instructions of the database statement and estimating a cost for executing the execution plan. The cost can comprise an encrypted data processing cost associated with a operation in the execution plan of executing an operation on encrypted data in a protected computing environment. The method can include estimating the encrypted data processing cost in the protected computing environment based on statistics generated in the protected computing environment about a database table. In response to estimating the cost for executing the execution plan, comparing the cost to estimated costs of alternative execution plans, selecting the lowest-cost plan for execution, and executing the lowest-cost execution plan.

Execution optimization of database statements involving encrypted data

A number of transmissions of secure data communicated between a secure trusted device and an unsecure untrusted device in a DBMS is controlled. The data is communicated for database transaction processing in the secure trusted device. The number of transmissions may be controlled by receiving, from the untrusted device, an encrypted key value of a key and a representation of an index of a B-tree structure, decrypting, at the trusted device, the key and one or more encrypted index values, and initiating a transmission, a pointer value that identifies a lookup position in the index for the key. The index comprises secure, encrypted index values. Other optimizations for secure processing are also described, including controlling available computation resources on a secure trusted device in a DBMS and controlling transmissions of secure data that is communicated between a secure trusted device and an unsecure untrusted device in a DBMS.

Controlling secure processing of confidential data in untrusted devices

A fuzzy joins system that is integrated in a database system generates fuzzy joins between records from two datasets. The fuzzy joins system includes a tokenizer to generate tokens for data records and a transformer to find transforms for the tokens. The fuzzy joins system invokes a signature generator, running within a runtime layer of the database system, to generate signatures for data records based on the tokens and their transforms. Subsequently, an equi-join operation joins the records from the two datasets with at least one equal signature. A similarity calculator, running within a runtime layer of the database system, computes a similarity measure using the token information of the joined records. If the similarity measure for any two records is above a threshold, the fuzzy joins system generates a fuzzy join between such two records.

Integrated fuzzy joins in database management systems

Growing community of inventors

Kirkland, WA, United States of America

Raghav Kaushik