With more and more news sources publishing news articles online, news recommendation systems are becoming increasingly popular in recent days. However, if a news recommender shows to the user many news articles that cover same story or talk about same persons or events, then the recommendations might become monotonous for the user. A possible way to handle this scenario is to diversify the recommended news articles so that the articles in the recommendation list are not too similar to each other. Existing research works in literature discuss how to use user-specific relevance of news articles and diversification to produce the final recommendations. To be able to compute user-specific relevance of articles, several recommender systems suggest that users have a login, or they set cookies to track page visits of the users. Often users are unsure whether the information collected via these means will be used for the sole purpose of news recommendation. Privacy of the identity and page access patterns is a concern for many users. In this work, we consider the problem where diverse recommendations are to be given for users, but no past data about users is collected or stored by the system. We use only article popularity to estimate the relevance. We identify few aspects of news articles on which diversification can be performed. These aspects are incorporated in an optimization framework. An approximation algorithm is used to generate the recommendations. Experimental analysis shows that in scenarios where no individual user data is stored or accessible by the recommender system, diversification results in higher click rates and increase in the number of satisfied users. Performances of the multiple diversification aspects and approaches are also analyzed.