Classifying Message Application Using Kotlin

Over the past few years, short message service (SMS) usage has significantly increased. This service is used to deliver text messages by billions of people. Service providers have launched a number of popular applications, including mobile banking, summons checkpoints, SMS chat, and others. This chapter explores the numerous SMS applications that are available to users and provides an outline of how this service is provided. We examine the causes of its success and the problems that need to be solved. We also look at upcoming trends and the difficulties that to improve this service, certain obstacles must be overcome. This chapter should help you understand how SMS applications work and what to expect from them going forwards given the improvements to current SMS and technological advancement. We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Our Rust implementation provides a fast and secure solution for the classification of unstructured text. Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340 ms in the case where the dictionary size of Bob’s model includes all words (n 5200) and Alice’s SMS has at most m 160 unigrams. In the case with n 369 and m 8 (the average of a spam SMS in the database), our solution takes only 21 ms.