Cloud present, pose a significant impact on current IT industry computing and big data, two disruptive trends at try and research communities. As the analysis of these data sets provides profound insights into a number of key areas of society (e.g., healthcare, medical, government services, e-research), the data sets are often shared or released to third party partners or the public. The privacy-sensitive information can be divulged with less effort by an adversary as the coupling of big data with public cloud environments disables some traditional privacy protection measures in cloud. party partners or the public. The privacy-sensitive information can be divulged with less effort by an adversary as the coupling of big data with public cloud environments disables some traditional privacy protection measures in cloud. In this paper, we model the problem of big data local recoding against proximity privacy breaches as a proximity-aware clustering (PAC) problem, and propose a scalable two-phase clustering approach accordingly. A proximity-aware distance is introduced over both quasi-identifier and sensitive attributes to facilitate clustering algorithms. To address the scalability problem, we propose a two-phase clustering approach consisting of the t-ancestors clustering and proximity-aware agglomerative clustering algorithms. The first phase splits an original data set into t partitions that contain similar data records in terms of quasi-identifiers. In the second phase, data partitions are locally recoded by the proximity-aware agglomerative clustering algorithm in parallel. We design the algorithms with Map Reduce in order to gain high scalability by performing data-parallel computation over multiple computing nodes in cloud. We evaluate our approach by conducting extensive experiments on real-world data sets.