Privacy preservation while undertaking collaborative distributed frequent itemset mining (PPDFIM) is an important research direction. The current state of the art for privacy preservation in distributed frequent itemset mining for secure sum in a horizontally partitioned data model comprises primarily public key based homomorphic schemes which are expensive in terms of the communication and computation cost. The nonpublic key based existing state-of-the-art scheme by Clifton et al. used for secure sum in PPDFIM is efficient but prone to security attacks. In this paper, we propose Shamir’s secret sharing based approaches and a symmetric key based scheme to calculate the secure sum in PPDFIM. These schemes are information theoretically secure under the standard assumptions. We further give a detailed theoretical and empirical evaluation of our proposed schemes for PPDFIM using a real market basket dataset. Our experimental analysis also shows that our schemes perform better in terms of the execution cost compared to the public key based scheme for secure sum in PPDFIM. 1. Introduction With numerous participants mining the data to gain insightful information useful to themselves, there is an inclination to share this information [1, 2]. With the increase in competition in businesses, it has also become essential to know how the competitors are performing. The primary concern in such a scenario is that each of the competitors does not want to disclose their individual data. Hence, privacy preservation is an important concern wherein collaborative distributed data mining needs to be undertaken. Privacy preservation in distributed data mining (PPDDM) is a significant secure multiparty computation (SMC) problem among other SMC problems [3–5]. SMC helps in knowing how the competitors are performing without compromising on either party’s privacy. The issue of SMC is such that only the data mining results of each of the sites that satisfy a certain function are known in the cumulative data. The confidential data of the collaborating parties remains private. In this paper, we focus on improving the state of the art of the privacy preserving techniques for PPDFIM (which is a subset of the area of PPDDM) in a horizontally partitioned or homogenous data model [6] considering semihonest adversaries as shown in Figure 1. Figure 1: Semihonest adversary model for secure sum in PPDFIM. Some important application scenarios of PPDFIM include medical data, market basket data, network data, data gathered by government agencies, and media related data [6]. An example of a
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