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Recent studies have revealed that sensitive and private attributes could be decoded from sEMG signals, which incurs significant privacy threats to the users of sEMG applications. Most researches so far focus on improving the accuracy and reliability of sEMG models, but much less attention has been paid to their privacy. To fill this gap, this paper implemented and evaluated a framework to optimize the sEMG-based data-sharing mechanism. Our primary goal is to remove sensitive attributes in the sEMG features before sharing them with primary tasks while maintaining the data utility. We disentangled the identity-invariant task-relevant representations from original sEMG features. We shared it with the downstream pattern recognition tasks to reduce the chance of sensitive attributes being inferred by potential attackers. The proposed method was evaluated on data from twenty subjects, with training and testing data acquired 3-25 days apart. Experimental results show that the disentangled representations significantly lower the success rate of identity inference attacks compared to the original feature and its sparse representations generated by the state-of-the-art feature projection methods. Furthermore, the utility of the disentangled representation is also evaluated in hand gesture recognition tasks, showing superior performance over other methods. This work shows that disentangled representations of sEMG signals are a promising solution for privacy-reserving applications.