Abstract: A method for preserving privacy with respect to modeling event sequence data is provided. The method includes: receiving information about a sequence of events; modeling the event sequence by a Hawkes process that has an intensity that includes an exogenous base intensity rate and an indigenous component that has an excitation rate and a decay rate; analyzing the received information; and determining estimated values of the exogenous base intensity rate and the excitation rate, such that an accuracy of the estimates corresponds to a length of time over which the sequence of events is observed. Differential privacy is introduced by adding noise to the sequence of events in order to preserve the privacy of individuals associated with the events, and a cost of the differential privacy is expressible as an additional length of observation time required to ensure the accuracy of the estimates.

Abstract: A method for using a Gaussian Process-based algorithm to approximate an optimal stopping of a time series that corresponds to a sequence of events is provided. The method includes: receiving information that relates to an event sequence; estimating, based on the received information, a first potential reward that is obtained by stopping the event sequence at a first time, and a set of respective second potential rewards that are obtained by stopping the event sequence at corresponding times; and determining, based on the estimated first and second potential rewards, an optimal time for stopping the event sequence. The event sequence may include a numerical sequence that is modeled as a statistical learning method via a Gaussian Process (GP) function and/or a deep GP function that indicates a probability density distribution of the items in the numerical sequence over a predetermined time interval.

Abstract: A method for using an artificial intelligence (AI) model to simulate a limit order book market in order to facilitate study and evaluation of trading strategies is provided. The method includes: receiving information that relates to a state of the market at a particular time; and determining, based on the information, a potential market action that is expected to occur. The determination is made by applying an AI algorithm that implements a machine learning technique to determine the potential market action. The AI algorithm is trained by using historical data that relates to the market.

Abstract: Methods and systems for using images that represent time-series data to forecast corresponding images depicting future values of the time-series data are provided. The method includes: receiving a set of time-series data; converting the set of time-series data into a partial first image that includes a blank region to which future data to be included in the first set of time-series data corresponds; and performing an inpainting operation with respect to the partial first image by generating pixels for filling in the blank region in order to produce an augmented version of the first image. A machine learning algorithm that is trained by using historical time-series data may be used to perform the inpainting operation.

Abstract: A method and a system for using differential privacy techniques to provide axe obfuscation with respect to information included in an inventory axe list of available securities is provided. The method includes: obtaining first information to be included in a first inventory axe list to be published on a particular day; retrieving second information included in a second inventory axe list that was published on the previous day and/or several previous days; obfuscating the obtained first information by applying an algorithm based on the difference between the first information and the second information; and publishing the first inventory axe list by transmitting the obfuscated first information to a plurality of intended recipients. The quality of obfuscation may be measured and controlled as a function of desired privacy level and potential cost.

Abstract: Systems and methods for privacy-preserving summarization of digital activity are disclosed. According to one embodiment in an information processing apparatus comprising at least one computer processor and at least one display, a privacy-preserving digital activity computer program performing the following: (1) capturing a blurred or pixelated screenshot of the at least one display; (2) identifying a plurality of computer application visible in the blurred or pixelated screenshot; (3) identifying a foreground or actively-used application out of the plurality computer applications in the blurred or pixelated screenshot; and (4) logging the visible computer applications and the foreground or actively-used application.

Abstract: Systems and methods for privacy-preserving inventory matching are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for inventory matching may include: (1) receiving, from each of a plurality of clients, a masked submission comprising an identification of at least one security to buy or sell and a desired quantity to buy or sell; (2) aggregating the masked submissions resulting in a sum of the desired quantities to buy or sell; (3) matching at least two of the clients to conduct a transaction based on aggregation and their respective masked submissions; and (4) conducing the transaction between the matched clients.

Abstract: A method and a system for using differential privacy techniques to provide axe obfuscation with respect to information included in an inventory axe list of available securities is provided. The method includes: obtaining first information to be included in a first inventory axe list to be published on a particular day; retrieving second information included in a second inventory axe list that was published on the previous day and/or several previous days; obfuscating the obtained first information by applying an algorithm based on the difference between the first information and the second information; and publishing the first inventory axe list by transmitting the obfuscated first information to a plurality of intended recipients. The quality of obfuscation may be measured and controlled as a function of desired privacy level and potential cost.

Abstract: A method for providing a data-driven tool for individualized financial planning is provided. The method includes: receiving first information that relates to a user, the first information including at least one financial goal of the user; applying, to the first information, a machine learning algorithm that is uses historical data that relates to financial outcomes; calculating, based on an output of the machine learning algorithm, a probability that the at least one financial goal of the user is achievable; and determining, based on an output the machine learning algorithm, a proposed sequence of actions to be taken by the user with respect to achieving the at least one financial goal. The method may further include applying an artificial intelligence (AI) algorithm that uses a Monte Carlo tree search (MCTS) technique with respect to potential user actions.

Abstract: A method for using images that represent time-series data to forecast future images depicting future values as pixelated information is provided. The method includes: receiving a first set of time-series data; converting the received first set of time-series data into a first image; and using the first image to forecast a future image depicting future values as pixelated information that corresponds to a future time interval Training sets of time-series data are used to generate historical data that provides input to a machine learning algorithm, which provides, as an output, a composite image that depicts the future values as pixelated information that reflects associated uncertainties in the value predictions.

Abstract: A method for assessing a value of an investment portfolio is provided. The method includes: receiving first information that relates to the investment portfolio from an investor; receiving second information that relates to a pricing model that is used by a financial institution for pricing investment assets; calculating metrics that relate to an estimated value of the investment portfolio based on the first information and the second information; and determining an assessed value of the investment portfolio based on the calculated metrics. The method may be implemented by using a secure multiparty computation technique by which the investor and the financial institution provide sensitive information as inputs to an algorithm without revealing the sensitive information to each other, thereby preserving the privacy of both parties.

Abstract: A method for learning a shared machine learning model while preserving privacy of individual participants is provided. The method includes: receiving, from each of a group of users, an encrypted user input; when a number of user inputs is greater than or equal to a threshold, transmitting, to each user, a list of the group of users; receiving, from each user, a message indicating a mutual agreement regarding a shared secret among the group; and when a number of received messages indicating the mutual agreement is greater than or equal to the threshold, determining information about the shared machine learning model by combining the received encrypted user inputs. The shared machine learning model facilitates a secure multi-party computation of a function that generates an updated version of the shared machine learning model.

Abstract: Systems and methods for preserving privacy in dark pool trading environments are provided. The methods include receiving buy orders that include encrypted buy order information; receiving sell orders that include encrypted sell order information; determining whether at least one received buy order matches with at least one received sell order; and when there is a match, executing a transaction based on the match. The determination is made without revealing the encrypted information to an operator of the dark pool, thereby preserving the confidentiality of the information until the transaction is executed.

Abstract: A method for privacy-preserving inventory matching may include: (1) receiving a plurality of axe submissions; (2) arranging the parties into data structures based on a direction in the party's axe submission; (3) sending each party's commitment to the other party; (4) receiving, from each party, output secret-shares of an arithmetized comparison circuit; (5) verifying that the output secret-shares of the arithmetized comparison circuit received from the parties match commitments to the output secret-shares sent by the respective opposite party; (6) identifying a minimal party based on the outputs of the arithmetized comparison circuit; (7) generating and sending a proof of the minimal party identification to the minimal party; (8) receiving a minimal quantity integer from the minimal party; (9) revealing the minimal quantity integer to the first party and the second party; and (10) executing the trade for the minimal quantity integer.

Abstract: A method for analyzing time-series data is provided. The method includes: identifying, from within a first set of time-series data, data sequences that correspond to patterns, each data sequence being representative of a predetermined time interval; converting the data sequences into respective pattern images; receiving a second set of time-series data; converting the second set of time-series data into an additional image; comparing each of the pattern images with a portion of the additional image that corresponds to a most recent time interval; and determining whether the portion of the additional image that corresponds to the most recent time interval corresponds to any of the patterns based on a result of the comparison.

Abstract: A method for forecasting a change in a market is provided. The method includes: using historical market data to generate a plurality of first images that correspond to a predetermined time sequence; generating, based on the plurality of first images, second images that correspond to a future time point with respect to the predetermined time sequence; and determining a prediction of future market data based on the second images. The generation of the second images and the prediction of the future market data are implemented by applying a convolutional neural network (CNN) algorithm that implements a stochastic latent residual video prediction (SRVP) technique with respect to a group of financial assets.

Abstract: A method and a system for using differential privacy techniques to provide axe obfuscation with respect to information included in an inventory axe list of available securities is provided. The method includes: obtaining first information to be included in a first inventory axe list to be published on a particular day; retrieving second information included in a second inventory axe list that was published on the previous day and/or several previous days; obfuscating the obtained first information by applying an algorithm based on the difference between the first information and the second information; and publishing the first inventory axe list by transmitting the obfuscated first information to a plurality of intended recipients. The quality of obfuscation may be measured and controlled as a function of desired privacy level and potential cost.

Abstract: Systems and methods for privacy-preserving inventory matching are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for inventory matching may include: (1) receiving, from each of a plurality of clients, a masked submission comprising an identification of at least one security to buy or sell and a desired quantity to buy or sell; (2) aggregating the masked submissions resulting in a sum of the desired quantities to buy or sell; (3) matching at least two of the clients to conduct a transaction based on aggregation and their respective masked submissions; and (4) conducing the transaction between the matched clients.

Abstract: Systems and methods for privacy-preserving summarization of digital activity are disclosed. According to one embodiment in an information processing apparatus comprising at least one computer processor and at least one display, a privacy-preserving digital activity computer program performing the following: (1) capturing a blurred or pixelated screenshot of the at least one display; (2) identifying a plurality of computer application visible in the blurred or pixelated screenshot; (3) identifying a foreground or actively-used application out of the plurality computer applications in the blurred or pixelated screenshot; and (4) logging the visible computer applications and the foreground or actively-used application.

Abstract: A method for using images that represent time-series data to forecast future images depicting future values as pixelated information is provided. The method includes: receiving a first set of time-series data; converting the received first set of time-series data into a first image; and using the first image to forecast a future image depicting future values as pixelated information that corresponds to a future time interval Training sets of time-series data are used to generate historical data that provides input to a machine learning algorithm, which provides, as an output, a composite image that depicts the future values as pixelated information that reflects associated uncertainties in the value predictions.