Privacy-Preserving and Outsourced Multi-User k-Means Clustering

Many techniques for privacy-preserving data mining (PPDM) have been investigated over the past decade. Often, the entities involved in the data mining process are end-users or organizations with limited computing and storage resources. As a result, such entities may want to refrain from participating in the PPDM process. To overcome this issue and to take many other benefits of cloud computing, outsourcing PPDM tasks to the cloud environment has recently gained special attention. We consider the scenario where n entities outsource their databases (in encrypted format) to the cloud and ask the cloud to perform the clustering task on their combined data in a privacy-preserving manner. We term such a process as privacy-preserving and outsourced distributed clustering (PPODC). In this paper, we propose a novel and efficient solution to the PPODC problem based on k-means clustering algorithm. The main novelty of our solution lies in avoiding the secure division operations required in computing cluster centers altogether through an efficient transformation technique. Our solution builds the clusters securely in an iterative fashion and returns the final cluster centers to all entities when a pre-determined termination condition holds. The proposed solution protects data confidentiality of all the participating entities under the standard semi-honest model. To the best of our knowledge, ours is the first work to discuss and propose a comprehensive solution to the PPODC problem that incurs negligible cost on the participating entities. We theoretically estimate both the computation and communication costs of the proposed protocol and also demonstrate its practical value through experiments on a real dataset.Comment: 16 pages, 2 figures, 5 table

Privacy Preserving Optics Clustering

OPTICS is a well-known density-based clustering algorithm which uses DBSCAN theme without producing a clustering of a data set openly, but as a substitute, it creates an augmented ordering of that particular database which represents its density-based clustering structure. This resulted cluster-ordering comprises information which is similar to the density based clustering’s conforming to a wide range of parameter settings. The same algorithm can be applied in the field of privacy-preserving data mining, where extracting the useful information from data which is distributed over a network requires preservation of privacy of individuals’ information. The problem of getting the clusters of a distributed database is considered as an example of this algorithm, where two parties want to know their cluster numbers on combined database without revealing one party information to other party. This issue can be seen as a particular example of secure multi-party computation and such sort of issues can be solved with the assistance of proposed protocols in our work along with some standard protocols

A Toolbox for privacy preserving distributed data mining

Distributed structure of individual data makes it necessary for data holders to perform collaborative analysis over the collective database for better data mining results. However each site has to ensure the privacy of its individual data, which means no information is revealed about individual values. Privacy preserving distributed data mining is utilized for that purpose. In this study, we try to draw more attention to the topic of privacy preserving data mining by showing a model which is realistic for data mining, and allows for very efficient protocols. We give two protocols which are useful tools in data mining: a protocol for Yaoѫs millionaires problem, and a protocol for numerical distance. Our solution to Yaoѫs millionaires problem is of independent interest since it gives a solution which improves on known protocols with respect to both computation complexity and communication overhead. This protocol can be used for different purposes in privacy preserving data mining algorithms such as comparison and equality test of data records. Our numerical distance protocol is also applicable to variety of algorithms. In this study we applied our numerical distance protocol in a privacy preserving distributed clustering protocol for horizontally partitioned data. We show application of our protocol over different attribute types such as interval-scaled,binary, nominal, ordinal, ratio-scaled, and alphanumeric. We present proof of security of our protocol, and explain communication, and computation complexity analysis indetail

Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing

In this paper, we propose a privacy preserving distributed clustering protocol for horizontally partitioned data based on a very efficient homomorphic additive secret sharing scheme. The model we use for the protocol is novel in the sense that it utilizes two non-colluding third parties. We provide a brief security analysis of our protocol from information theoretic point of view, which is a stronger security model. We show communication and computation complexity analysis of our protocol along with another protocol previously proposed for the same problem. We also include experimental results for computation and communication overhead of these two protocols. Our protocol not only outperforms the others in execution time and communication overhead on data holders, but also uses a more efficient model for many data mining applications

Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data

The k-means clustering is one of the most popular clustering algorithms in data mining. Recently a lot of research has been concentrated on the algorithm when the dataset is divided into multiple parties or when the dataset is too large to be handled by the data owner. In the latter case, usually some servers are hired to perform the task of clustering. The dataset is divided by the data owner among the servers who together perform the k-means and return the cluster labels to the owner. The major challenge in this method is to prevent the servers from gaining substantial information about the actual data of the owner. Several algorithms have been designed in the past that provide cryptographic solutions to perform privacy preserving k-means. We provide a new method to perform k-means over a large set using multiple servers. Our technique avoids heavy cryptographic computations and instead we use a simple randomization technique to preserve the privacy of the data. The k-means computed has exactly the same efficiency and accuracy as the k-means computed over the original dataset without any randomization. We argue that our algorithm is secure against honest but curious and passive adversary.Comment: 19 pages, 4 tables. International Conference on Information Systems Security. Springer, Cham, 201