Abstract: A method for privacy preservation for machine learning networks includes splitting a trained neural network into a first part and a second part. The first part is a privacy preservation (PP) encoder and the second part is a PP machine learning (ML) model. The method further includes retraining the PP encoder and the PP ML model.

Abstract: Systems and method for training a shared machine learning (ML) model. A method includes generating, by a first entity, a data transformation function; sharing, by the first entity, the data transformation function with one or more second entities; creating a first private dataset, by the first entity, by applying the data transformation function to a first dataset of the first entity; receiving one or more second private datasets, by the first entity, from the one or more second entities, each second private dataset having been created by applying the data transformation function to a second dataset of the second entity; and training a machine learning (ML) model using the first private dataset and the one or more second private datasets to produce a trained ML model.

Abstract: Systems and method for training a shared machine learning (ML) model. A method includes generating, by a first entity, a data transformation function; sharing, by the first entity, the data transformation function with one or more second entities; creating a first private dataset, by the first entity, by applying the data transformation function to a first dataset of the first entity; receiving one or more second private datasets, by the first entity, from the one or more second entities, each second private dataset having been created by applying the data transformation function to a second dataset of the second entity; and training a machine learning (ML) model using the first private dataset and the one or more second private datasets to produce a trained ML model.

Abstract: For providing an efficient network use and resource allocation within the network a method for operating a network is provided, wherein user network traffic is controlled by an operator, comprising the following steps: a) monitoring user network traffic data on a per user basis, b) using said network traffic data in a learning process for providing a prediction of user network traffic on a per user basis, and c) controlling user network traffic under consideration of said prediction, including allocating network resources under consideration of said prediction to one or more users, preferably for providing a definable Quality of Service, QoS, per at least ne of said one or more users and/or per at least one other user. Further, a corresponding network is claimed.

Abstract: A method for synchronizing an identity of a user between a network observer and a third party includes: reading, by the network observer, a connection request sent from the user to the third party, the connection request including a host field having a host part corresponding to the third party and a sub-host part corresponding to identification information of the user; and associating, by the network observer in a database, the identification information of the user derived from the sub-host part with the identity of the user and an identity of the third party.

Abstract: A method preserves privacy in an HTTP communication between a client and a server. The method includes: intercepting an HTTP request that is sent from the client to the server; extracting a cookie from the HTTP request, the cookie including a cookie name and a cookie value; splitting the cookie value into information segments according to a split pattern; and modifying one or more of the information segments based on predefined modification rules. The split pattern for the cookie value is received from a cookie format analyzer. The cookie format analyzer selects the split pattern by: generating multiple lists of data formats based on the cookie name, and selecting one of the lists as the split pattern based on similarity and frequency features associated with the data formats.

Abstract: For providing an efficient network use and resource allocation within the network a method for operating a network is provided, wherein user network traffic is controlled by an operator, comprising the following steps: a) monitoring user network traffic data on a per user basis, b) using said network traffic data in a learning process for providing a prediction of user network traffic on a per user basis, and c) controlling user network traffic under consideration of said prediction, including allocating network resources under consideration of said prediction to one or more users, preferably for providing a definable Quality of Service, QoS, per at least ne of said one or more users and/or per at least one other user. Further, a corresponding network is claimed.

Abstract: Systems and method for training a shared machine learning (ML) model. A method includes generating, by a first entity, a data transformation function; sharing, by the first entity, the data transformation function with one or more second entities; creating a first private dataset, by the first entity, by applying the data transformation function to a first dataset of the first entity; receiving one or more second private datasets, by the first entity, from the one or more second entities, each second private dataset having been created by applying the data transformation function to a second dataset of the second entity; and training a machine learning (ML) model using the first private dataset and the one or more second private datasets to produce a trained ML model.

Abstract: A method for providing recommendations to users includes obtaining stored data structure triples and actual ratings associated with the data structure triples; training a machine learning model using the stored data structure triples and associated actual ratings, wherein training the machine learning model includes generating user, product, and review representations based on the stored data structure triples and their associated ratings; predicting, by the machine learning model, ratings using the generated user, product, and review representations; and making recommendations based on the predicted ratings.

Abstract: A method preserves privacy in an HTTP communication between a client and a server. The method includes: intercepting an HTTP request that is sent from the client to the server; extracting a cookie from the HTTP request, the cookie including a cookie name and a cookie value; splitting the cookie value into information segments according to a split pattern; and modifying one or more of the information segments based on predefined modification rules. The split pattern for the cookie value is received from a cookie format analyzer. The cookie format analyzer selects the split pattern by: generating multiple lists of data formats based on the cookie name, and selecting one of the lists as the split pattern based on similarity and frequency features associated with the data formats.

Abstract: A method for determining an identity of a URL visited by a user from a vantage point in a network in which network traffic is encrypted includes determining a host to model, generating a list of URLs hosted by the host to model, repeatedly retrieving web resources referenced by the list of URLs hosted by the host to model and generating a network traffic signature upon each retrieval, generating a data feature for each of the generated network traffic signatures, and training, using the generated data features, a classifier corresponding to the host to model, wherein the classifier is configured to determine an identity of the visited URL from a signature of network traffic produced by the retrieval of a resource referenced by the visited URL.

Abstract: A method for preserving privacy in an HTTP communication between a client and a server includes: intercepting an HTTP request that is sent from the client to the server; extracting a cookie from the HTTP request, the cookie including a cookie name and a cookie value; splitting the cookie value into information segments; and modifying one or more of the information segments based on predefined modification rules.

Abstract: A method for assigning a server to provide a resource to a client in a distributed network includes receiving a request for the resource from the client. A network metric is measured at different points in the network. The network metric measurements are input to a deep learning model. Using the model, the network metric is predicted between the client and each of a plurality candidate servers which have the resource and have not had a prior connection with the client. One of the candidate servers is assigned to provide the resource to the client based on the predictions of the network metric.

Abstract: A method for synchronizing an identity of a user between a network observer and a third party includes: reading, by the network observer, a connection request sent from the user to the third party, the connection request including a host field having a host part corresponding to the third party and a sub-host part corresponding to identification information of the user; and associating, by the network observer in a database, the identification information of the user derived from the sub-host part with the identity of the user and an identity of the third party.

Abstract: A method for data inspection includes upon receiving an out-of-order part of a sequence of parts, checking the out-of-order part for matching in forward and backward direction of the sequence; temporarily saving a forward state and a backward state of said both checkings; and upon receiving an in-order part of the sequence, checking the in-order part for matching in the forward direction of the sequence and if the in-order packet is within a gap between the first part and the last part of the sequence the in-order part is also checked for matching in backward direction of the sequence. The backward state or the forward and backward state are updated or temporarily saved.

Abstract: A method for preserving privacy in an HTTP communication between a client and a server includes: intercepting an HTTP request that is sent from the client to the server; extracting a cookie from the HTTP request, the cookie including a cookie name and a cookie value; splitting the cookie value into information segments; and modifying one or more of the information segments based on predefined modification rules.

Abstract: A method for assigning a server to provide a resource to a client in a distributed network includes receiving a request for the resource from the client. A network metric is measured at different points in the network. The network metric measurements are input to a deep learning model. Using the model, the network metric is predicted between the client and each of a plurality candidate servers which have the resource and have not had a prior connection with the client. One of the candidate servers is assigned to provide the resource to the client based on the predictions of the network metric.

Abstract: A method for providing recommendations to users includes obtaining stored data structure triples and actual ratings associated with the data structure triples; training a machine learning model using the stored data structure triples and associated actual ratings, wherein training the machine learning model includes generating user, product, and review representations based on the stored data structure triples and their associated ratings; predicting, by the machine learning model, ratings using the generated user, product, and review representations; and making recommendations based on the predicted ratings.

Abstract: A method for data inspection includes upon receiving an out-of-order part of a sequence of parts, checking the out-of-order part for matching in forward and backward direction of the sequence; temporarily saving a forward state and a backward state of said both checkings; and upon receiving an in-order part of the sequence, checking the in-order part for matching in the forward direction of the sequence and if the in-order packet is within a gap between the first part and the last part of the sequence the in-order part is also checked for matching in backward direction of the sequence. The backward state or the forward and backward state are updated or temporarily saved.

Abstract: A method for determining an identity of a URL visited by a user from a vantage point in a network in which network traffic is encrypted includes determining a host to model, generating a list of URLs hosted by the host to model, repeatedly retrieving web resources referenced by the list of URLs hosted by the host to model and generating a network traffic signature upon each retrieval, generating a data feature for each of the generated network traffic signatures, and training, using the generated data features, a classifier corresponding to the host to model, wherein the classifier is configured to determine an identity of the visited URL from a signature of network traffic produced by the retrieval of a resource referenced by the visited URL.