Abstract: Technologies for providing event-level data privacy for streaming post analytics data include, in some embodiments, receiving a data stream that includes instances of count data collected over a time interval, computing a true count breakdown that includes a set of sub-counts of non-public user interface interactions on the post, creating a noisy count breakdown by applying at least one differential privacy mechanism to the set of sub-counts, and streaming the noisy count breakdown instead of the true count breakdown to a computing device. At least one of the sub-counts is a count that is associated with a particular value of an attribute that has different possible values. The attribute is associated with the non-public user interface interactions on the post.

Abstract: Embodiments described herein provide a technique to crowdsource labeling of training data for a machine learning model while maintaining the privacy of the data provided by crowdsourcing participants. Client devices can be used to generate proposed labels for a unit of data to be used in a training dataset. One or more privacy mechanisms are used to protect user data when transmitting the data to a server. The server can aggregate the proposed labels and use the most frequently proposed labels for an element as the label for the element when generating training data for the machine learning model. The machine learning model is then trained using the crowdsourced labels to improve the accuracy of the model.

Abstract: Technologies for providing event-level data privacy for streaming post analytics data include, in some embodiments, receiving a data stream that includes instances of count data collected over a time interval, computing a true count breakdown that includes a set of sub-counts of non-public user interface interactions on the post, creating a noisy count breakdown by applying at least one differential privacy mechanism to the set of sub-counts, and streaming the noisy count breakdown instead of the true count breakdown to a computing device. At least one of the sub-counts is a count that is associated with a particular value of an attribute that has different possible values. The attribute is associated with the non-public user interface interactions on the post.

Abstract: Technologies for digital content distribution include creating a data set that includes a plurality of entity data records each comprising a plurality of attribute values. By applying a first data security technique to the data set, a first query term and a matching subset of the plurality of entity data records are determined. A second data security technique is applied to the matching subset. When output of the second data security technique satisfies a first noisy threshold, the first query term is added to a set of query terms. A third data security technique is applied to the matching subset. When output of the third data security technique satisfies a second noisy threshold, the set of query terms is expanded to include a second query term. The set of query terms is used to perform the digital content distribution.

Abstract: Embodiments described herein provide a technique to crowdsource labeling of training data for a machine learning model while maintaining the privacy of the data provided by crowdsourcing participants. Client devices can be used to generate proposed labels for a unit of data to be used in a training dataset. One or more privacy mechanisms are used to protect user data when transmitting the data to a server. The server can aggregate the proposed labels and use the most frequently proposed labels for an element as the label for the element when generating training data for the machine learning model. The machine learning model is then trained using the crowdsourced labels to improve the accuracy of the model.

Abstract: In an embodiment, the disclosed technologies include receiving a query that requests aggregate information about entity event data relating to digital content delivered digitally by an entity management system to entities of the entity management system, the query associated with a requester account; determining a first privacy allocation for the requester account; determining a first privacy value, the first privacy value computed based on the query and a selected privacy algorithm; deducting the first privacy value from the first privacy allocation to produce a first privacy balance; causing executing of the query on the entity event data and providing a result set in response to the query only if the first privacy balance indicates that the first privacy allocation has not been depleted.

Abstract: Techniques for ensuring differential privacy in top-K selection are provided. In one technique, multiple items and multiple counts are identified in response to a query. For each count, which corresponds to a different item, a noise value is generated and added to the count to generate a noisy value, and the noisy value is added to a set of noisy values that is initially empty. A particular noise value is generated for a particular count and added to the particular count to generate a noisy threshold. The particular noise value is generated using a different technique than the technique used to generate each noise value in the set. Based on the noisy threshold, a subset of the noisy values is identified, where each noisy value in the subset is less than the noisy threshold. A response to the query is generated that excludes items that correspond to the subset.

Abstract: Embodiments described herein provide techniques to encode sequential data in a privacy preserving manner before the data is sent to a sequence learning server. The server can then determine aggregate trends within an overall set of users, without having any specific knowledge about the contributions of individual users. The server can be used to learn new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. The server can also learn other sequential data including typed, autocorrected, revised text sequences, sequences of application launches, sequences of purchases on an application store, or other sequences of activities that can be performed on an electronic device.

Abstract: In an embodiment, the disclosed technologies include receiving a query that requests aggregate information about entity event data relating to digital content delivered digitally by an entity management system to entities of the entity management system, the query associated with a requester account; determining a first privacy allocation for the requester account; determining a first privacy value, the first privacy value computed based on the query and a selected privacy algorithm; deducting the first privacy value from the first privacy allocation to produce a first privacy balance; causing executing of the query on the entity event data and providing a result set in response to the query only if the first privacy balance indicates that the first privacy allocation has not been depleted.

Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to receive, at a client device, a machine learning model from a server, detect a usage pattern for a content item, store an association between the content item and the detected usage pattern in local data, train the machine learning model using local data for the content item with the detected usage pattern to generate a trained machine learning model, generate an update for the machine learning model, privatize the update for the machine learning model, and transmit the privatized update for the machine learning model to the server.

Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to perform operations comprising receiving a machine learning model from a server at a client device, training the machine learning model using local data at the client device, generating an update for the machine learning model, the update including a weight vector that represents a difference between the received machine learning model and the trained machine learning model, privatizing the update for the machine learning model, and transmitting the privatized update for the machine learning model to the server.

Abstract: Embodiments described herein provide a technique to crowdsource labeling of training data for a machine learning model while maintaining the privacy of the data provided by crowdsourcing participants. Client devices can be used to generate proposed labels for a unit of data to be used in a training dataset. One or more privacy mechanisms are used to protect user data when transmitting the data to a server. The server can aggregate the proposed labels and use the most frequently proposed labels for an element as the label for the element when generating training data for the machine learning model. The machine learning model is then trained using the crowdsourced labels to improve the accuracy of the model.

Abstract: Embodiments described herein provide techniques to encode sequential data in a privacy preserving manner before the data is sent to a sequence learning server. The server can then determine aggregate trends within an overall set of users, without having any specific knowledge about the contributions of individual users. The server can be used to learn new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. The server can also learn other sequential data including typed, autocorrected, revised text sequences, sequences of application launches, sequences of purchases on an application store, or other sequences of activities that can be performed on an electronic device.

Abstract: One embodiment provides for a mobile electronic device comprising a non-transitory machine-readable medium to store instructions, the instructions to cause the mobile electronic device to receive a set of labeled data from a server; receive a unit of data from the server, the unit of data of a same type of data as the set of labeled data; determine a proposed label for the unit of data via a machine learning model on the mobile electronic device, the machine learning model to determine the proposed label for the unit of data based on the set of labeled data from the server and a set of unlabeled data associated with the mobile electronic device; encode the proposed label via a privacy algorithm to generate a privatized encoding of the proposed label; and transmit the privatized encoding of the proposed label to the server.