Patents by Inventor Aleksandar Nikolov

Aleksandar Nikolov has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20190057224
    Abstract: The privacy of linear queries on histograms is protected. A database containing private data is queried. Base decomposition is performed to recursively compute an orthonormal basis for the database space. Using correlated (or Gaussian) noise and/or least squares estimation, an answer having differential privacy is generated and provided in response to the query. In some implementations, the differential privacy is ?-differential privacy (pure differential privacy) or is (?,?)-differential privacy (i.e., approximate differential privacy). In some implementations, the data in the database may be dense. Such implementations may use correlated noise without using least squares estimation. In other implementations, the data in the database may be sparse. Such implementations may use least squares estimation with or without using correlated noise.
    Type: Application
    Filed: October 24, 2018
    Publication date: February 21, 2019
    Inventors: Li Zhang, Kunal Talwar, Aleksandar Nikolov
  • Publication number: 20180364584
    Abstract: A substrate table configured to support a substrate for exposure in an immersion lithographic apparatus, the substrate table including: a support body having a support surface configured to support the substrate; and a cover ring fixed relative to the support body and configured to surround, in plan view, the substrate supported on the support surface, wherein the cover ring has an upper surface, wherein at least a portion of the upper surface is configured so as to alter the stability of a meniscus of immersion liquid when moving along the upper surface towards the substrate.
    Type: Application
    Filed: November 2, 2016
    Publication date: December 20, 2018
    Applicant: ASML NETHERLANDS B.V.
    Inventors: Daan Daniel Johannes Antonius VAN SOMMEREN, Coen Hubertus Matheus BALTIS, Harold Sebastiaan BUDDENBERG, Giovanni Luca GATTOBIGIO, Johannes Cornelis Paulus MELMAN, Günes NAKIBOGLU, Theodorus Wilhelmus POLET, Walter Theodorus Matheus STALS, Yuri Johannes Gabriël VAN DE VIJVER, Josephus Peter VAN LIESHOUT, Jorge Alverto VIEYRA SALAS, Aleksandar Nikolov ZDRAVKOV
  • Patent number: 10121024
    Abstract: The privacy of linear queries on histograms is protected. A database containing private data is queried. Base decomposition is performed to recursively compute an orthonormal basis for the database space. Using correlated (or Gaussian) noise and/or least squares estimation, an answer having differential privacy is generated and provided in response to the query. In some implementations, the differential privacy is ?-differential privacy (pure differential privacy) or is (?,?)-differential privacy (i.e., approximate differential privacy). In some implementations, the data in the database may be dense. Such implementations may use correlated noise without using least squares estimation. In other implementations, the data in the database may be sparse. Such implementations may use least squares estimation with or without using correlated noise.
    Type: Grant
    Filed: May 4, 2017
    Date of Patent: November 6, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Li Zhang, Kunal Talwar, Aleksandar Nikolov
  • Publication number: 20170235974
    Abstract: The privacy of linear queries on histograms is protected. A database containing private data is queried. Base decomposition is performed to recursively compute an orthonormal basis for the database space. Using correlated (or Gaussian) noise and/or least squares estimation, an answer having differential privacy is generated and provided in response to the query. In some implementations, the differential privacy is ?-differential privacy (pure differential privacy) or is (?,?)-differential privacy (i.e., approximate differential privacy). In some implementations, the data in the database may be dense. Such implementations may use correlated noise without using least squares estimation. In other implementations, the data in the database may be sparse. Such implementations may use least squares estimation with or without using correlated noise.
    Type: Application
    Filed: May 4, 2017
    Publication date: August 17, 2017
    Inventors: Li ZHANG, Kunal TALWAR, Aleksandar NIKOLOV
  • Patent number: 9672364
    Abstract: The privacy of linear queries on histograms is protected. A database containing private data is queried. Base decomposition is performed to recursively compute an orthonormal basis for the database space. Using correlated (or Gaussian) noise and/or least squares estimation, an answer having differential privacy is generated and provided in response to the query. In some implementations, the differential privacy is ?-differential privacy (pure differential privacy) or is (?,?)-differential privacy (i.e., approximate differential privacy). In some implementations, the data in the database may be dense. Such implementations may use correlated noise without using least squares estimation. In other implementations, the data in the database may be sparse. Such implementations may use least squares estimation with or without using correlated noise.
    Type: Grant
    Filed: March 15, 2013
    Date of Patent: June 6, 2017
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Li Zhang, Kunal Talwar, Aleksandar Nikolov
  • Publication number: 20140283091
    Abstract: The privacy of linear queries on histograms is protected. A database containing private data is queried. Base decomposition is performed to recursively compute an orthonormal basis for the database space. Using correlated (or Gaussian) noise and/or least squares estimation, an answer having differential privacy is generated and provided in response to the query. In some implementations, the differential privacy is ?-differential privacy (pure differential privacy) or is (?,?)-differential privacy (i.e., approximate differential privacy). In some implementations, the data in the database may be dense. Such implementations may use correlated noise without using least squares estimation. In other implementations, the data in the database may be sparse. Such implementations may use least squares estimation with or without using correlated noise.
    Type: Application
    Filed: March 15, 2013
    Publication date: September 18, 2014
    Inventors: Li Zhang, Kunal Talwar, Aleksandar Nikolov