CLAIMS
`
`1.
`
`A method, comprising:
`
`receiving a request from a client to generate a differentially private random
`
`forest classifier trained using a set of restricted data stored by a private
`
`database system, the request identifying a level of differential privacy
`
`corresponding to the request, the identified level of differential privacy
`
`comprising privacy parameters a and 6, wherein 8 describes a degree of
`
`information released about the set of restricted data due to the request and
`
`6 describes an improbability of the request satisfying (s)-differential
`
`privacy,
`
`generating the differentially private random forest classifier in response to the
`
`request, generating the classifier comprising:
`
`determining a number of decision trees comprising the differentially
`
`private random forest classifier,
`
`generating the determined number of decision trees, generating a
`
`decision tree comprising:
`
`generating a set of splits based on features of the set of restricted
`
`data,
`
`determining an information gain for each split of the set of splits,
`
`selecting a split from the set of splits using an exponential
`
`mechanism based at least in part on the determined information
`
`gains of the splits in the set and at least one of the privacy
`
`parameters, and
`
`57
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`32552/39379/FW/9958284.13
`
`
`
`1.
`
`A method, comprising:
`
`receiving a request from a client to generate a differentially private random
`
`forest classifier trained using a set of restricted data stored by a private
`
`database system, the request identifying a level of differential privacy
`
`corresponding to the request, the identified level of differential privacy
`
`comprising privacy parameters a and 6, wherein 8 describes a degree of
`
`information released about the set of restricted data due to the request and
`
`6 describes an improbability of the request satisfying (s)-differential
`
`privacy,
`
`generating the differentially private random forest classifier in response to the
`
`request, generating the classifier comprising:
`
`determining a number of decision trees comprising the differentially
`
`private random forest classifier,
`
`generating the determined number of decision trees, generating a
`
`decision tree comprising:
`
`generating a set of splits based on features of the set of restricted
`
`data,
`
`determining an information gain for each split of the set of splits,
`
`selecting a split from the set of splits using an exponential
`
`mechanism based at least in part on the determined information
`
`gains of the splits in the set and at least one of the privacy
`
`parameters, and
`
`57
`
`32552/39379/FW/9958284.13
`
`
`
`adding the selected split to the decision tree at a node, and
`
`providing the differentially private random forest classif1er to the client;
`
`wherein the differentially private random forest classif1er is used to determine
`
`whether an entity has a condition.
`
`The method of claim 1, wherein determining the information gain for a split of the
`
`set of splits comprises evaluating:
`
`tio
`tio
`— Zini Eon—ilogz n_i’
`
`wherein no is determined by:
`
`Eli tio ,
`
`wherein n; is determined by:
`
`20 tio :
`
`wherein m is determined by either:
`
`Zini 07’ Zono ,
`
`wherein 0 is a numeric representation of a classification category, 1' is a
`
`numeric representation of a feature of data in the set of restricted data, and
`
`fig is a number of data tuples in the set of restricted data having both the
`
`classification category represented by 0 and the feature represented by 1'.
`
`The method of claim 2, wherein selecting a split from the set of splits using the
`
`exponential mechanism is further based on a sensitivity, wherein the sensitivity of
`
`the exponential mechanism is determined by:
`
`logz (nt + 1) +
`
`
`1
`
`logZ '
`
`58
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`32552/39379/FW/9958284.13
`
`
`
`4.
`
`The method of claim 1, wherein the generated decision tree comprises a plurality
`
`of leaf nodes representing classification categories, and further comprising, for a
`
`leaf node representing a classification category:
`
`determining a differentially private count of entities in the set of restricted
`
`data in the classification category represented by the leaf node,
`
`wherein providing the differentially private random forest classifier to the
`
`client comprises providing the differentially private count of entities in the
`
`classification category represented by the leaf node.
`
`5.
`
`The method of claim 1, wherein generating the random forest classifier in
`
`response to the request is based on one or more model parameters, including at
`
`least one of a number of decision trees to be used in the classifier, a number of
`
`splits to include in the decision trees, a maXimum tree height, and a utility gain
`
`threshold, and wherein each of the one or more model parameters is at least one of
`
`a default value and a value included in the request.
`
`6.
`
`The method of claim 1,
`
`wherein the restricted data stores records comprising rows and columns,
`
`wherein the rows are associated with patients having a medical condition,
`
`wherein the columns contain values describing health data for the patients,
`
`and
`
`wherein performing the modified set of operations on the accessed set of data
`
`to produce the differentially private result set comprises estimating
`
`whether a new patient has the medical condition.
`
`59
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`32552/39379/FW/9958284.13
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`
`
`7.
`
`The method of claim 1;
`
`wherein the set of data stores records comprising rows and columns;
`
`wherein the rows are associated with customers having a financial account;
`
`wherein the columns contain values describing financial data for the
`
`customers; and
`
`wherein performing the modified set of operations on the accessed set of data
`
`to produce the differentially private result set comprises estimating
`
`whether a new customer can perform a financial transaction.
`
`A non-transitory computer-readable storage medium storing computer program
`
`instructions executable by a processor to perform operations; the operations
`
`comprising:
`
`receiving a request from a client to generate a differentially private random
`
`forest classifier trained using a set of restricted data stored by a private
`
`database system; the request identifying a level of differential privacy
`
`corresponding to the request; the identified level of differential privacy
`
`comprising privacy parameters a and 6; wherein 8 describes a degree of
`
`information released about the set of restricted data due to the request and
`
`6 describes an improbability of the request satisfying (s)-differential
`
`privacy;
`
`generating the differentially private random forest classifier in response to the
`
`request; generating the classifier comprising:
`
`determining a number of decision trees comprising the differentially
`
`private random forest classifier;
`
`60
`
`32552/39379/FW/9958284.13
`
`
`
`generating the determined number of decision trees; generating a
`
`decision tree comprising:
`
`generating a set of splits based on features of the set of restricted
`
`data;
`
`determining an information gain for each split of the set of splits;
`
`selecting a split from the set of splits using an exponential
`
`mechanism based at least in part on the determined information
`
`gains of the splits in the set and at least one of the privacy
`
`parameters; and
`
`adding the selected split to the decision tree at a node; and
`
`providing the differentially private random forest classif1er to the client;
`
`wherein the differentially private random forest classif1er is used to determine
`
`whether an entity has a condition.
`
`The non-transitory computer-readable storage medium of claim 8; wherein
`
`determining the information gain for a split of the set of splits comprises
`
`evaluating:
`
`tio
`tio
`2171120 ni logz “i 3
`
`wherein no is determined by:
`
`Eli tio ,
`
`wherein n; is determined by:
`
`20 tio :
`
`wherein m is determined by either:
`
`Zini 07’ Zono ,
`
`61
`
`32552/39379/FW/9958284.13
`
`
`
`wherein 0 is a numeric representation of a classification category, 1' is a
`
`numeric representation of a feature of data in the set of restricted data, and
`
`fig is a number of data tuples in the set of restricted data having both the
`
`classification category represented by 0 and the feature represented by 1'.
`
`10.
`
`The non-transitory computer-readable storage medium of claim 9, wherein
`
`selecting a split from the set of splits using the exponential mechanism is further
`
`based on a sensitivity, wherein the sensitivity of the exponential mechanism is
`
`determined by:
`
`
`1
`log2 (nt + 1) +10g2 .
`
`ll.
`
`The non-transitory computer-readable storage medium of claim 8, wherein the
`
`generated decision tree comprises a plurality of leaf nodes representing
`
`classification categories, and the operations further comprising, for a leaf node
`
`representing a classification category:
`
`determining a differentially private count of entities in the set of restricted
`
`data in the classification category represented by the leaf node,
`
`wherein providing the differentially private random forest classifier to the
`
`client comprises providing the differentially private count of entities in the
`
`classification category represented by the leaf node.
`
`62
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`32552/39379/FW/9958284.13
`
`
`
`12.
`
`The non-transitory computer-readable storage medium of claim 8, wherein
`
`generating the random forest classifier in response to the request is based on one
`
`or more model parameters, including at least one of a number of decision trees to
`
`be used in the classifier, a number of splits to include in the decision trees, a
`
`maximum tree height, and a utility gain threshold, and wherein each of the one or
`
`more model parameters is at least one of a default value and a value included in
`
`the request.
`
`l3.
`
`The non-transitory computer-readable storage medium of claim 8,
`
`wherein the restricted data stores records comprising rows and columns,
`
`wherein the rows are associated with patients having a medical condition,
`
`wherein the columns contain values describing health data for the patients,
`
`and
`
`wherein performing the modified set of operations on the accessed set of data
`
`to produce the differentially private result set comprises estimating
`
`whether a new patient has the medical condition.
`
`14.
`
`The non-transitory computer-readable storage medium of claim 8,
`
`wherein the set of data stores records comprising rows and columns,
`
`wherein the rows are associated with customers having a financial account,
`
`wherein the columns contain values describing financial data for the
`
`customers, and
`
`63
`
`32552/39379/FW/9958284.13
`
`
`
`wherein performing the modified set of operations on the accessed set of data
`
`to produce the differentially private result set comprises estimating
`
`whether a new customer can perform a financial transaction.
`
`15.
`
`A system comprising:
`
`a processor for executing computer program instructions; and
`
`a non-transitory computer-readable storage medium storing computer program
`
`instructions executable by the processor to perform operations comprising:
`
`receiving a request from a client to generate a differentially private
`
`random forest classifier trained using a set of restricted data stored by
`
`a private database system, the request identifying a level of differential
`
`privacy corresponding to the request, the identified level of differential
`
`privacy comprising privacy parameters a and 6, wherein 8 describes a
`
`degree of information released about the set of restricted data due to
`
`the request and 6 describes an improbability of the request satisfying
`
`(s)—differential privacy,
`
`generating the differentially private random forest classifier in response to
`
`the request, generating the classifier comprising:
`
`determining a number of decision trees comprising the
`
`differentially private random forest classifier,
`
`generating the determined number of decision trees, generating a
`
`decision tree comprising:
`
`generating a set of splits based on features of the set of
`
`restricted data,
`
`64
`
`32552/39379/FW/9958284.13
`
`
`
`determining an information gain for each split of the set of
`
`splits;
`
`selecting a split from the set of splits using an exponential
`
`mechanism based at least in part on the determined
`
`information gains of the splits in the set and at least one of
`
`the privacy parameters; and
`
`adding the selected split to the decision tree at a node; and
`
`providing the differentially private random forest classif1er to the client;
`
`wherein the differentially private random forest classifier is used to
`
`determine whether an entity has a condition.
`
`l6.
`
`The system of claim 15; wherein determining the information gain for a split of
`
`the set of splits comprises evaluating:
`
`tio
`tio
`_2ini Eon—ilogz n_i’
`
`wherein no is determined by:
`
`2i tio ,
`
`wherein n; is determined by:
`
`20 tio :
`
`wherein m is determined by either:
`
`Zini 07’ Zono ,
`
`wherein 0 is a numeric representation of a classification category; 1' is a
`
`numeric representation of a feature of data in the set of restricted data; and
`
`fig is a number of data tuples in the set of restricted data having both the
`
`classification category represented by 0 and the feature represented by 1'.
`
`65
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`32552/39379/FW/9958284.13
`
`
`
`17.
`
`The system of claim 16, wherein selecting a split from the set of splits using the
`
`exponential mechanism is further based on a sensitivity, wherein the sensitivity of
`
`the exponential mechanism is determined by:
`
`
`1
`log2 (nt + 1) +10g2 .
`
`18.
`
`The system of claim 15, wherein the generated decision tree comprises a plurality
`
`of leaf nodes representing classification categories, and further comprising, for a
`
`leaf node representing a classification category:
`
`determining a differentially private count of entities in the set of restricted
`
`data in the classification category represented by the leaf node,
`
`wherein providing the differentially private random forest classifier to the
`
`client comprises providing the differentially private count of entities in the
`
`classification category represented by the leaf node.
`
`19.
`
`The system of claim 15,
`
`wherein the restricted data stores records comprising rows and columns,
`
`wherein the rows are associated with patients having a medical condition,
`
`wherein the columns contain values describing health data for the patients,
`
`and
`
`wherein performing the modified set of operations on the accessed set of data
`
`to produce the differentially private result set comprises estimating
`
`whether a new patient has the medical condition.
`
`20.
`
`The system of claim 15,
`
`wherein the set of data stores records comprising rows and columns,
`
`66
`
`32552/39379/FW/9958284.13
`
`
`
`wherein the rows are associated with customers having a financial account;
`
`wherein the columns contain values describing financial data for the
`
`customers; and
`
`wherein performing the modified set of operations on the accessed set of data
`
`to produce the differentially private result set comprises estimating
`
`whether a new customer can perform a financial transaction.
`
`67
`
`32552/39379/FW/9958284.13
`
`
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