Abstract: Creating compatible anonymized data sets by performing with machine learning equipment that operates a machine learning model by defining data types of variables of a data set; identifying quasi-identifiers for the data set; defining reidentification sensitivity of all or any targeted subset of the individual variables and quasi-identifiers; defining missing data handling rules for the individual variables; defining allowed data transformations including generalization and use of synthesized data; optimizing quasi-identifier selection, use of synthesized data and a choice of data transformations to minimize information loss and maximize privacy metrics based on the data set; the allowed data transformations; and the missing data handling rules; training the machine learning model using the data set according to the defined data types; the optimized quasi-identifier selection; the optimized use of synthesized data; and the choice of data transformations; and anonymizing the data set using the training of the m

Abstract: Creating compatible anonymized data sets by performing with machine learning equipment that operates a machine learning model by defining data types of variables of a data set; identifying quasi-identifiers for the data set; defining reidentification sensitivity of all or any targeted subset of the individual variables and quasi-identifiers; defining missing data handling rules for the individual variables; defining allowed data transformations including generalization and use of synthesized data; optimizing quasi-identifier selection, use of synthesized data and a choice of data transformations to minimize information loss and maximize privacy metrics based on the data set; the allowed data transformations; and the missing data handling rules; training the machine learning model using the data set according to the defined data types; the optimized quasi-identifier selection; the optimized use of synthesized data; and the choice of data transformations; and anonymizing the data set using the training of the m

Abstract: Efficient and effective communications are initiated from the selection, at a first user device, of a link in a resource. Based on this selection, a communications request is transmitted to a server. In addition, identification information of a second user device (such as a telephone number or an Internet Protocol (IP) address) is transmitted to the server. As a result of this request and identification information, a communication response is received at the second user device. This communications response may be of various types, such as a voice call, a message, or a rich call.

Abstract: Devices, systems and methods are provided for simultaneous battery charging and data transfer in a mobile terminal. The invention utilizes power line communication networks to transfer data between a mobile terminal and other remote digital devices. As such, the present invention can provide data transfer to and from the mobile terminal while the mobile terminal is undergoing requisite battery charging.

Abstract: A terminal for interacting with a service provider via signaling tag includes a controller capable of actively operating an application. The controller is also capable of receiving information regarding a signaling tag, including information relating to a service type, at least partially over an air interface. Then, the controller is capable of accessing the service. Thereafter, the controller is capable of performing a predefined action, such as downloading data from the service and/or uploading data to the service, based upon the information relating to the service type, the application actively operating on the terminal and a state of the application.