There is provided a computer-implemented method for identifying functions within software applications, comprising: receiving a software application for identification; automatically identifying third-party data acquisition functions embedded within the software application, the third-party data acquisition functions communicating with a remote third-party server; and providing the identified third-party data acquisition functions for the software application.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for identifying functions within software applications, comprising: receiving a software application for identification; automatically identifying third-party data acquisition functions embedded within the software application, the third-party data acquisition functions communicating with a remote third-party server; and providing the identified third-party data acquisition functions for the software application. 2. The method of claim 1, further comprising automatically classifying the software application based on the identified third-party data acquisition functions. 3. The method of claim 1, wherein automatically identifying comprises automatically identifying concealed embedded third-party data acquisition functions based on a preselected group of classification features extracted from the software applications, the preselected group of classifying features corresponding to the concealed embedded third-party data acquisition functions. 4. The method of claim 2, wherein classifying comprises classifying by a classifier, the classifier evaluating the software application based on a selected group of extracted classifying features, the extracted classifying features selected from a plurality of features so that the extracted classification features are based on third-party data acquisition functions embedded within the software application. 5. The method of claim 1, wherein the third-party data acquisition functions are provided as part of a software development kit (SDK) for development of the software application. 6. The method of claim 1, wherein the third-party includes at least one member of a group consisting of: ad network, advertiser, hacker, spy, market information collector, malicious party. 7. The method of claim 1, wherein the third-party data acquisition functions are function calls to the third-party remove server and/or application programming interfaces (API) communicating with the third-party remote server. 8. The method of claim 1, wherein the software application is received at a client terminal for local run-time classification by the client terminal. 9. The method of claim 2, further comprising providing a classification type denoting one of a plurality of ad networks. 10. The method of claim 1, further comprising installing or removing the software application based on the identified third-party data acquisition function. 11. A computer-implemented method for generating data for identifying embedded third-party data acquisition functions within software applications on a client terminal, comprising: identifying, at a central server, a plurality of features from each of a plurality of training software applications, each of the plurality of training software applications contains third-party data acquisition functions embedded therein, the third-party data acquisition functions communicating with a remote third-party server, the embedded third-party data acquisition functions being concealed so that similar third-party data acquisition functions corresponding to a same third-party have different identities between at least two training software applications embedding concealed third-party data acquisition functions from the same third-party; identifying a group of classifying features from the plurality of features, the group of classifying features corresponding to the embedded third-party data acquisition functions within each of the plurality of training software applications; and providing the group of classifying features to a client terminal, for identification of third-party data acquisition functions embedded within a software application locally by the client terminal. 12. The method of claim 11, further comprising generating a classifier for evaluating software applications based on the group of classifying features; and providing the classifier to a client terminal, for feature extraction and classification of a software application locally by the client terminal. 13. The method of claim 11, wherein the group of classifying features is selected to correspond to concealed embedded third-party data acquisition functions. 14. The method of claim 12, wherein the classifier is a multiclass classifier for classifying the software application into one of a plurality of different third-parties. 15. The method of claim 12, wherein the classifier is a single-class classifier for classifying the software applications as having third-party data acquisition functions embedded therein or not. 16. The method of claim 11, wherein identifying a group of classifying features from the plurality of features comprises: labeling each of the plurality of training software applications with a predetermined classification category; generating a set of values, by applying a machine learning software module to the plurality of features and corresponding labels, certain values within the set of values corresponding to certain features of the plurality of features; identifying the group of classifying features based on a sub-set of values from the set of values, each classifying feature from the group of features corresponding to at least one value from the sub-set of values, the set-of values corresponding to the embedded third-party data acquisition functions. 17. The method of claim 16, wherein the set of values includes at least one member of a group consisting of: a vector of coefficients, a matrix of coefficients, a set of decision rules, a tree of decision rules. 18. The method of claim 16, wherein identifying comprises identifying the sub-set of values as the highest ranked absolute values of the set of values. 19. The method of claim 18, further comprising identifying classifying types of each of the values of the set of values, and identifying comprises selecting the highest ranked absolute values of the set of values for each identified classification type. 20. The method of claim 16, wherein identifying comprises identifying groups of similar third-party data acquisition functions based on cardinality within the set of values. 21. A system for identifying third-party data acquisition functions embedded within software applications, comprising: a client terminal comprising: a client processor; a first non-transitory memory having stored thereon program modules for local instruction execution by the client processor, comprising: an identification module for automatically identifying third-party data acquisition functions embedded within the software application, the third-party data acquisition functions communicating with a remote third-party server. 22. The system of claim 21, further comprising a classification module for automatically classifying the software application based on the identified third-party data acquisition functions embedded therein, to generate a classification type for the software application. 23. The system of claim 21, wherein the client terminal further comprises: a client network node for communicating with a server network node interface over a network. 24. The system of claim 21, further comprising: a network connected central server; a second non-transitory memory having stored thereon program modules for instruction execution by the central server, comprising: a module for generating classification data for identifying third-party data acquisition functions embedded therein, and/or classifying software applications based on third-party data acquisition function embedded therein; a server network node interface for providing the classification data to a client terminal, for identification of third-party data acquisition functions within a software application and/or classification of the software application locally by the client terminal. 25. The system of claim 24, wherein the network connected central server further comprises: a module for identifying a plurality of features from each of a plurality of training software applications, each of the plurality of training software applications contains third-party acquisition functions embedded therein, the third-party acquisition functions communicating with a remote third-party server, the embedded third-party acquisition functions being concealed so that similar third-party acquisition functions corresponding to a same third-party have different identities between at least two training software applications embedding concealed third-party acquisition functions from the same third-party; a module for identifying a group of classifying features from the plurality of features, the group of classifying features corresponding to the embedded third-party acquisition functions within each of the plurality of training software applications; and a module for generating a classifier for evaluating software applications based on the group of classifying features. 26. The system of claim 21, wherein the client terminal further comprises: a feature extractor module for local run-time execution by the client processor, the feature extractor module programmed for extracting features from a software application based on a group of classifying features received from a central server. 27. The system of claim 24, further comprising a labeling module for labeling of software application with one of a plurality of ad networks, the labeled software applications used for generating a multi-class version of a classifier, the labeling module stored on the second memory. 28. The system of claim 24, further comprising a feature extractor module stored on the second memory, the feature extraction module for extraction of features of software applications into complete feature vectors, the group of classifying features selected from the complete feature vector. 29. The system of claim 21, wherein the client terminal is resource limited, having insufficient resources for local run-time extraction of a complete feature vector of a plurality of features from the software application. 30. The system of claim 23, wherein the client terminal includes at least one member of a group consisting of: mobile phone, Smartphone, tablet, portable media player, e-reader.