Text Clustering technique is widely used in both centralized and distributed environments for information retrieval. Centralized approaches are used in traditional text clustering algorithms. In such approaches, clustering is performed on a dedicated node and also they are not suitable for deployment in large distributed networks. This centralized approach require high processing time and retrieving time during searching due to scalability of users. To overcome this, the Probabilistic text clustering for peer to peer(PCP2P) algorithm was introduced. It provides improved scalability by using a probabilistic approach for assigning documents to clusters. Only most relevant clusters are considered for comparison with each document. PCP2P alone does not provide any security for the system. It only ensures efficient information retrieval of text data by assigning document to most relevant cluster. This project is implemented within a medical research environment. Here, the system is implemented using this PCP2P algorithm and an enhanced version of a security related technique called LZW. Enhanced LZW technique can ensure secure transmission within the system. This technique includes hiding information directly in compression codes. Lempel -Ziv-Welch (LZW) coding is a well known lossless compression algorithm is simple and does not require prior analysis of the source or send extra information to the decoder. This introduces a new input table which contains 255 contents. This method, reversibly embeds data in LZW compression codes by modifying the value of the compression codes. The value of the LZW code either remains unchanged or is changed to the original value of the LZW code plus the LZW dictionary size according to the data to be embedded. Index Terms: Text clustering, K-means, P2P network, Distributed network, DHT, Lossless data compression, LZW code