The design of scalar quantization for distributed binary decision in presence of an eavesdropper (Eve) is investigated. An encoder/quantizer (Alice) observes a memoryless source and communicate via a public noiseless rate-limited channel with the detector (Bob) who has also access to a correlated analog source. Bob can take advantage of both informations to perform a binary decision on the joint probability law of these observations. Eve is further assumed to have access to a different correlated analog source and perfectly observe the information bits sent by Alice. This paper evaluates the various tradeoffs between the probabilities of error (on the decision) depending on the amount of information leakage from Alice to Eve. The Bhattacharyya distance; one of the distances measuring the difference between two probability distributions; is taken as a criterion to optimize the scalar quantizer subject to a tolerable constraint on the information leakage at the level of Eve. Numerical results for memoryless Gaussian sources demonstrate the performance of the proposed quantization method.