Abstract: Various examples are directed to systems and methods for obscuring directional data to improve privacy. An example system may access a first unit of directional data. The example system may select a sampled value from an angular cumulative distribution function (CDF) of a random distribution. The example system may use the selected sampled value to generate a random sample from the random distribution and apply the random sample to the first unit of directional data to generate a first obscured unit of directional data.

Abstract: Various examples are directed to systems and methods for obscuring private information in input data. A system may apply an encoder model to an input data unit to generate a latent space representation of the input data unit. The system may apply multi-dimensional noise to the latent space representation of the input data unit, the multi-dimensional noise having a first value in a first latent space dimension and a second value different than the first value in a second latent space dimension. The system may apply a decoder model to the latent space representation of the input data unit to generate an obscured data unit.

Abstract: Techniques for automatically selecting a differential privacy parameter in a neural network for data obfuscation are disclosed. In some embodiments, a computer system performs a method comprising: obtaining a privacy loss parameter of differential privacy; and training a neural network to perform data obfuscation operations, the training of the neural network comprising learning a variance parameter using the privacy loss parameter, the data obfuscation operations comprising: encoding input data into a latent space representation of the input data, the encoding of the input data comprising inferring latent space parameters of a latent space distribution based on the input data and sampling data from the latent space distribution, the latent space distribution being based on the variance parameter; and decoding the sampled data of the latent space representation into output data.

Abstract: Techniques for implementing a differentially private variational autoencoder for data obfuscation are disclosed. In some embodiments, a computer system performs operations comprising: encoding input data into a latent space representation of the input data, the encoding of the input data comprising: inferring latent space parameters of a latent space distribution based on the input data, the latent space parameters comprising a mean and a standard deviation, the inferring of the latent space parameters comprising bounding the mean within a finite space and using a global value for the standard deviation, the global value being independent of the input data; and sampling data from the latent space distribution; and decoding the sampled data of the latent space representation into output data.

Abstract: Directional data often conveys particularly sensitive information, such as user location. To protect user privacy, directional data is replaced with modified directional data that is selected based on the actual directional data, a privacy parameter, and a probability distribution on an n-sphere. In this way, the modified directional data value is useful when aggregated with other modified directional data values, but does not infringe the privacy of the directional data of the user.

Abstract: Directional data often conveys particularly sensitive information, such as user location. To protect user privacy, directional data is replaced with modified directional data that is selected based on the actual directional data, a privacy parameter, and a probability distribution on an n-sphere. In this way, the modified directional data value is useful when aggregated with other modified directional data values, but does not infringe the privacy of the directional data of the user.

Abstract: Various examples are directed to systems and methods for obscuring personal information in a sensor data stream. A system may apply an encoder model to the sensor data stream to generate a latent space representation of the sensor data stream. The system may also apply a noise-scaling parameter to the latent space representation of the sensor data stream and apply a decoder model to the latent space representation of the sensor data stream to generate an obscured data stream.

Abstract: A method of producing an anonymized vector for a text mining task in lieu of a feature vector is disclosed. A vocabulary is created from a corpus of documents, each of the corpus of documents having a context that is similar to a set of target documents. The set of target documents is received. The feature vector is generated from a first document of the set of target documents. The feature vector is transformed into a composition vector. A synthetic vector is constructed based on the composition vector. The synthetic vector is shared as the anonymized vector in lieu of the feature vector.

Abstract: A method of producing an anonymized vector for a text mining task in lieu of a feature vector is disclosed. A vocabulary is created from a corpus of documents, each of the corpus of documents having a context that is similar to a set of target documents. The set of target documents is received. The feature vector is generated from a first document of the set of target documents. The feature vector is transformed into a composition vector. A synthetic vector is constructed based on the composition vector. The synthetic vector is shared as the anonymized vector in lieu of the feature vector.