Preserving privacy in Information Retrieval (IR) remains a significant issue for users when interacting with Information Retrieval System (IRSs). Conducting a private search when an IRS does not cooperate towards the protection of the user privacy can lead to unwanted information disclosure through the analysis of the queries sent to the system. Recent investigations in Natural Language Processing (NLP) and IR have adopted the use of \(\varepsilon \) -Differential Privacy (DP) to obfuscate the real information need contained in the user queries. Although privacy is protected from a formal point of view, such methods do not consider the fact that the obfuscations can be irrelevant if the lexical or semantic meaning of the obfuscated terms remains unchanged with respect to the real user text. This paper outlines the author’s PhD research in designing new techniques based on \(\varepsilon \) -DP for preserving the real user information need when interacting with (IRSs) that aim to disclose private information.