Abstract: Aspects of the current subject matter are directed to privacy-preserving demand estimation. According to an aspect, a method includes receiving, by a first party, a request to provide a projection; receiving, by the first party, a key; in response to the request, performing, by the first party, a partial projection using private data of the first party without sharing the private data with at least one other party; encrypting, by the first party, the partial projection using the key; sending, by the first party, the encrypted partial projection to an aggregator; combining, by the aggregator; the encrypted partial projection provided by the first party with at least one other encrypted partial projection provided by the at least one other party to generate an encrypted combined projection. Related systems, methods, and articles of manufacture are also disclosed.

Abstract: In an example embodiment, a differentially private function is computed via secure computation. Secure computation allows multiple parties to compute a function without learning details about the data. The differentially private function is performed via probability distribution, which then permits computation of a result that is likely to be very close to the actual value without being so exact that it can be used to deduce the underlying data itself.

Abstract: According to an aspect, a method may include receiving a candidate value; in response to a received candidate value matching one of the entries in the table, incrementing a corresponding count; in response to the received candidate value not matching one of the entries in the table and the table not exceeding a threshold size, adding an entry to the table; in response to the received candidate value not matching one of the entries in the table and the table exceeding the threshold size, decrementing the counts in the table and deleting entries having a count of zero; adding noise to the corresponding counts in the entries of the table and deleting any noisy corresponding counts less than a threshold value; and outputting at least a portion of the table as the top-k value result set.

Abstract: Aspects of the current subject matter are directed to performing privacy-preserving analytics over sensitive data without sharing plaintext data and without requiring a trusted third party. Implementations provide for utilizing a trusted execution environment within a server to compute the privacy-preserving result. Data owners via user devices send their encrypted data directly to an enclave managed by a trusted execution environment, without the server and the cloud service provider for the server seeing the plaintext data. The enclave computes the analytics directly on the data and releases the privacy-preserving result that can be ensured by code analysis and remote attestation from all parties.

Abstract: Aspects of the current subject matter are directed to performing privacy-preserving analytics over sensitive data without sharing plaintext data and without requiring a trusted third party. Implementations provide for utilizing a trusted execution environment within a server to compute the privacy-preserving result. Data owners via user devices send their encrypted data directly to an enclave managed by a trusted execution environment, without the server and the cloud service provider for the server seeing the plaintext data. The enclave computes the analytics directly on the data and releases the privacy-preserving result that can be ensured by code analysis and remote attestation from all parties.

Abstract: Aspects of the current subject matter are directed to performing privacy-preserving analytics over sensitive data without sharing plaintext data. According to an aspect, a system includes at least one data processor and at least one memory storing instructions which, when executed by the at least one data processor, result in operations including: receiving, from each of a plurality of clients, a utility score and a partial noise value; performing, based on the received utility scores and the partial noise values, a secure multi-party computation of a privacy-preserving statistic, the performing of the secure multi-party computation of the privacy-preserving statistic further comprising determining a noisy utility score for each data value in a domain of output values and selecting a highest noise utility score from the determined noisy utilities scores; and providing, based on the selected highest utility score, an output value for the privacy-preserving statistic.

Abstract: Techniques for efficient, accurate, and secure computation of a differentially private median of the union of two large confidential datasets are disclosed. In some example embodiments, a computer-implemented method comprises obtaining secret shares of a first dataset of a first entity, secret shares of a second dataset of a second entity, secret shares of gap values for the first dataset, secret shares of gap values for the second dataset, secret shares of probability mass values for the first dataset, and secret shares of probability mass values for the second dataset. The probability mass values may be computed via an exponential mechanism. In some example embodiments, the computer-implemented method further comprises determining a median of a union of the first dataset and the second dataset using an inverse transform sampling algorithm based on the obtained secret shares, and then performing a function of a networked computer system using the determined median.

Abstract: Aspects of the current subject matter are directed to performing privacy-preserving analytics over sensitive data without sharing plaintext data and without requiring a trusted third party. Implementations provide for utilizing a trusted execution environment within a server to compute the privacy-preserving result. Data owners via user devices send their encrypted data directly to an enclave managed by a trusted execution environment, without the server and the cloud service provider for the server seeing the plaintext data. The enclave computes the analytics directly on the data and releases the privacy-preserving result that can be ensured by code analysis and remote attestation from all parties.

Abstract: In an example embodiment, a differentially private function is computed via secure computation. Secure computation allows multiple parties to compute a function without learning details about the data. The differentially private function is performed via probability distribution, which then permits computation of a result that is likely to be very close to the actual value without being so exact that it can be used to deduce the underlying data itself.

Abstract: Techniques for efficient, accurate, and secure computation of a differentially private median of the union of two large confidential datasets are disclosed. In some example embodiments, a computer-implemented method comprises obtaining secret shares of a first dataset of a first entity, secret shares of a second dataset of a second entity, secret shares of gap values for the first dataset, secret shares of gap values for the second dataset, secret shares of probability mass values for the first dataset, and secret shares of probability mass values for the second dataset. The probability mass values may be computed via an exponential mechanism. In some example embodiments, the computer-implemented method further comprises determining a median of a union of the first dataset and the second dataset using an inverse transform sampling algorithm based on the obtained secret shares, and then performing a function of a networked computer system using the determined median.

Abstract: A system for differential privacy is provided. In some implementations, the system performs operations comprising receiving a plurality of indices for a plurality of perturbed data points, which are anonymized versions of a plurality of unperturbed data points, wherein the plurality of indices indicate that the plurality of unperturbed data points are identified as presumed outliers. The plurality of perturbed data points can lie around a first center point and the plurality of unperturbed data points can lie around a second center point. The operations can further comprise classifying a portion of the presumed outliers as true positives and another portion of the presumed outliers as false positives, based upon differences in distances to the respective first and second center points for the perturbed and corresponding (e.g., same index) unperturbed data points. Related systems, methods, and articles of manufacture are also described.

Abstract: Systems and methods are provided for sending a request to register a data offer from a data owner to participate in a distributed ledger, the request including information associated with the data offer and a privacy budget for the data offer, and wherein the information associated with the data offer and the privacy budget is stored in the distributed ledger and the data offer is accessible by third parties to the data owner. The systems and method further providing for receiving a request, associated with a third party computer, to access data associated with the data offer, processing a data request associated with the request to access data, based on determining that there is sufficient privacy budget to allow access to the data associated with the request to access data, to produce result data, anonymizing the result data, and updating the distributed ledger.

Abstract: Systems and methods are provided for sending a request to register a data offer from a data owner to participate in a distributed ledger, the request including information associated with the data offer and a privacy budget for the data offer, and wherein the information associated with the data offer and the privacy budget is stored in the distributed ledger and the data offer is accessible by third parties to the data owner. The systems and method further providing for receiving a request, associated with a third party computer, to access data associated with the data offer, processing a data request associated with the request to access data, based on determining that there is sufficient privacy budget to allow access to the data associated with the request to access data, to produce result data, anonymizing the result data, and updating the distributed ledger.

Abstract: A system for differential privacy is provided. In some implementations, the system performs operations comprising receiving a plurality of indices for a plurality of perturbed data points, which are anonymized versions of a plurality of unperturbed data points, wherein the plurality of indices indicate that the plurality of unperturbed data points are identified as presumed outliers. The plurality of perturbed data points can lie around a first center point and the plurality of unperturbed data points can lie around a second center point. The operations can further comprise classifying a portion of the presumed outliers as true positives and another portion of the presumed outliers as false positives, based upon differences in distances to the respective first and second center points for the perturbed and corresponding (e.g., same index) unperturbed data points. Related systems, methods, and articles of manufacture are also described.