Abstract- Collaborative computing utilizes different informationservers to mutually finish information investigation, e.g., factual examinationand surmising. One significant deterrent for it lies in privacy concern, whichis straightforwardly connected with hubs' investment and the devotion of gotinformation. Existing privacy-preserving ideal models for distributed computingand circulated information total just give hubs homogeneous privacy securitywithout thought of hubs' various trust degrees to various information servers.We propose a two-phase structure that registers the normal worth while preservingheterogeneous privacy for hubs' private information. The new test is that inthe reason of meeting privacy necessities, we ought to ensure the proposedsystem has a similar calculation precision with existing privacy-mindfularrangements. In this paper, hubs acquire heterogeneous privacy assurancedespite various information servers through one-shot clamor bother. In light ofthe meaning of KL privacy, we determine the logical articulations of theprivacy preserving degrees (PPDs) and measure the connection between variousPPDs. At that point, we acquire the shut structure articulation of calculationexactness. Moreover, a proficient motivating force system is proposed toaccomplish improved calculation exactness at the point when information servershave fixed financial plans. At last, broad reenactments are directed to confirmthe got hypothetical outcomes.
Keywords: Collaborative computing,average consensus, privacy preservation, incentive mechanism