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Sensor networks are often deployed in environments where malicious nodes present. Among all possible forms of the attacks threatening the sensor networks, in this work, we focus on traffic analysis attacks. Typically, in performing traffic analysis, an attacker will eavesdrop on-going wireless transmission and analyze contents and timing instances of the transmissions so to infer critical events or to trace valuable assets in the network (e.g. data sources or sinks). The paper presents a probabilistic approach to shape the sensor network traffic to decorrelate time instances in transmissions. The security properties of the approach are studied both analytically and empirically, showing strong protection in high probability