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Data mining is a technique where massive amounts of both sensitive and non-sensitive data are collected and examined. While distributing such private data, privacy preserving becomes an important issue. Various methods and techniques have been introduced in privacy preserving data mining to undertake this problem. The main intention of privacy preserving is to extract the knowledge without disclosing private data and it also concerns about the sequential release of data. Sequential data helps in predicting the next occurrence which leads to violating the privacy of individual data. In this paper, we briefly surveyed sequential pattern hiding, k-anonymity, data perturbation and secure sum computation techniques to address the issues of privacy preserving data sharing.

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The multi-hop routing in wireless sensor networks (WSNs) offers little protection against identity deception through replaying routing information. An adversary can exploit this defect to launch various harmful or even devastating attacks against the routing protocols, including sinkhole attacks, wormhole attacks and Sybil attacks. The situation is further aggravated by mobile and harsh network conditions. Traditional cryptographic techniques or efforts at developing trust-aware routing protocols do not effectively address this severe problem. To secure the WSNs against adversaries misdirecting the multi-hop routing, that has been designed and implemented TARF, a robust trust-aware routing framework for dynamic WSNs. Without tight time synchronization or known geographic information, This project provides trustworthy, time efficient and energyefficient route. Most importantly, TARF proves effective against those harmful attacks developed out of identity deception; the resilience of TARF is verified through extensive evaluation with both implementation and empirical experiments on large-scale WSNs under various scenarios including mobile and RF-shielding network conditions.