DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 – 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Krieger, 2022/0121767.
As per claim 1, Krieger teaches a method comprising:
receiving a query directed at a shared dataset, the query identifying a operation (access request, [0025]);
accessing, by at least one hardware processor, a set of data from the shared dataset to perform the operation, the set of data comprising data accessed from a table of the shared dataset (dataset including rows and columns, [0020]);
determining that an aggregation constraint policy is attached to the table, the aggregation constraint policy restricting output of data values stored in the table (access policies for rows and columns, [0020]);
determining, based on a context of the query, that the aggregation constraint policy should be enforced in relation to the query (filtering based on credentials, [0021]);
assigning a specified noise level to the shared dataset based on determining that the aggregation constraint policy should be enforced (redaction based on policies and credentials, [0025-0026]); and
generating an output to the query based on the set of data and the operation, the output to the query comprising data values added to the table based on the specified noise level (redacting and censoring cells in a presentation, [0068]).
As per claim 2, Krieger teaches wherein assigning the specified noise level to the shared dataset further comprises:
adjusting an amount of noise based on a privacy level, wherein the privacy level determines a degree of privacy preservation for the shared dataset (redaction based on policies and credentials, [0025-0026]).
As per claim 3, Krieger teaches further comprising:
determining the privacy level based on at least one of a trust level of a querying party, sensitivity of the shared dataset, risk potential of data exposure, or accuracy of aggregated results (sensitive data, [0068]).
As per claim 4, Krieger teaches wherein the receiving the query directed at the shared dataset further comprises:
determining that the query is attempting to directly access sensitive information; and
rejecting the query when the query is in violation of the aggregation constraint policy (removing sensitive information, [0068]).
As per claim 5, Krieger teaches further comprising:
providing an interface for a data provider to review and adjust the aggregation constraint policy and the specified noise level; and
controlling an amount of noise per entity granularity based on the context of the query or a context of the aggregation constraint policy (adjusting privileges, [0021], adjusting policies [0043][0065]).
As per claim 6, Krieger teaches further comprising:
applying, based on the aggregation constraint policy, user-specified noise to aggregate functions of the query on a table at runtime; and
identifying a minimum group size, based on the aggregation constraint policy, to be satisfied before returning the output (adjusting privileges, [0021], adjusting policies [0043][0065]).
As per claim 7, Krieger teaches further comprising:
generating aggregate results of the query; and
injecting noise into the aggregate results of the query based on the specified noise level (redacting and censoring, [0068][0025-0026]).
Claims 8-14 are rejected based on claims 1-7
Claims 15 – 20 are rejected based on claims 1-6
Conclusion
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/OLUGBENGA O IDOWU/Primary Examiner, Art Unit 2494
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