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Saeed Samet

Memorial University of Newfoundland, Faculty of Medicine, Faculty Member

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Saeed Samet is a member of the Faculty of Medicine as an Assistant Professor at the e-Health Research Unit at the discipline of Family Medicine from March 2012. He is also cross-appointed to the Department of Computer Science.

Prior to that, he was a Post-Doctoral Fellow at the e-Health Information Laboratory at the Children Hospital of Eastern Ontario Research Institute (CHEO-RI) from 2010 to 2012. During his post-doc fellowship he designed and developed several secure protocols for various health applications. He received his Ph.D. in Computer Science from the University of Ottawa in 2010 and his thesis was in Privacy-Preserving Data Mining, in which he has proposed and designed different protocols on privacy-preserving methods for standard data mining and machine learning techniques.

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Papers by Saeed Samet

Managing digital rights using JSON

by Stephen Downes, Saeed Samet, and Rodrigue Savoie

Abstract Prior art in the expression of digital rights using XML is demonstrated to require a pro... more Abstract Prior art in the expression of digital rights using XML is demonstrated to require a process of interpretation or parsing, as is characteristic with language processing, and in addition to be subject to the cross-domain scripting problem. The expression of digital rights using JSON, however, represents a novel approach, bypassing the need for language processing, and in addition, solving the cross-domain scripting problem.

A secure revocable personal health record system with policy-based fine-grained access control

2015 13th Annual Conference on Privacy, Security and Trust (PST), 2015

SNACK: An efficient intrusion detection system in Mobile Ad-Hoc Network based on the Selective-Negative Acknowledgement algorithm

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), 2015

Privacy-Preserving Classification and Clustering Using Secure Multi-Party Computation

Nowadays, data mining and machine learning techniques are widely used in electronic applications ... more Nowadays, data mining and machine learning techniques are widely used in electronic applications in different areas such as e-government, e-health, e-business, and so on. One major and very crucial issue in these type of systems, which are normally distributed among two or more parties and are dealing with sensitive data, is preserving the privacy of individual’s sensitive information. Each

Privacy Preserving k-Means Clustering in Multi-Party Environment

Secrypt, 2007

Extracting meaningful and valuable knowledge from databases is often done by various data mining ... more Extracting meaningful and valuable knowledge from databases is often done by various data mining algorithms. Nowadays, databases are distributed among two or more parties because of different reasons such as physical and geographical restrictions and the most important issue is privacy. Related data is normally maintained by more than one organization, each of which wants to keep its individual information private. Thus, privacy-preserving techniques and protocols are designed to perform data mining on distributed environments when privacy is highly concerned. Cluster analysis is a technique in data mining, by which data can be divided into some meaningful clusters, and it has an important role in different fields such as bio-informatics, marketing, machine learning, climate and medicine. k-means Clustering is a prominent algorithm in this category which creates a one-level clustering of data. In this paper we introduce privacy-preserving protocols for this algorithm, along with a protocol for Secure comparison, known as the Millionaires' Problem, as a sub-protocol, to handle the clustering of horizontally or vertically partitioned data among two or more parties.

SUFFUSE: Simultaneous Fuzzy-Rough Feature-Sample Selection

Journal of Advances in Information Technology, 2015

Privacy-Preserving Logistic Regression

Journal of Advances in Information Technology, 2015

A privacy preserving protocol for tracking participants in phase I clinical trials

Journal of Biomedical Informatics, 2015

Some phase 1 clinical trials offer strong financial incentives for healthy individuals to partici... more Some phase 1 clinical trials offer strong financial incentives for healthy individuals to participate in their studies. There is evidence that some individuals enroll in multiple trials concurrently. This creates safety risks and introduces data quality problems into the trials. Our objective was to construct a privacy preserving protocol to track phase 1 participants to detect concurrent enrollment. A protocol using secure probabilistic querying against a database of trial participants that allows for screening during telephone interviews and on-site enrollment was developed. The match variables consisted of demographic information. The accuracy (sensitivity, precision, and negative predictive value) of the matching and its computational performance in seconds were measured under simulated environments. Accuracy was also compared to non-secure matching methods. The protocol performance scales linearly with the database size. At the largest database size of 20,000 participants, a query takes under 20s on a 64 cores machine. Sensitivity, precision, and negative predictive value of the queries were consistently at or above 0.9, and were very similar to non-secure versions of the protocol. The protocol provides a reasonable solution to the concurrent enrollment problems in phase 1 clinical trials, and is able to ensure that personal information about participants is kept secure.

Privacy-preserving protocols for perceptron learning algorithm in neural networks

2008 4th International IEEE Conference Intelligent Systems, 2008

Neural networks have become increasingly important in areas such as medical diagnosis, bio-inform... more Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy of individual's private information and sensitive data. In this paper, we propose two secure protocols for perceptron learning algorithm when input data is horizontally and vertically partitioned among the parties. These protocols can be applied in both linearly separable and non-separable datasets, while not only data belonging to each party remains private, but the final learning model is also securely shared among those parties. Parties then can jointly and securely apply the constructed model to predict the output corresponding to their target data. Also, these protocols can be used incrementally, i.e. they process new coming data, adjusting the previously constructed network.

Secure two and multi-party association rule mining

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009

Abstract Association rule mining provides useful knowledge from raw data in different application... more

Secure Surveillance of Antimicrobial Resistant Organism Colonization or Infection in Ontario Long Term Care Homes

PLoS ONE, 2014

Background: There is stigma attached to the identification of residents carrying antimicrobial re... more

Managing Digital Rights Using JSON

2010 7th IEEE Consumer Communications and Networking Conference, 2010

Prior art in the expression of digital rights using XML is demonstrated to require a process of i... more Prior art in the expression of digital rights using XML is demonstrated to require a process of interpretation or parsing, as is characteristic with language processing, and in addition to be subject to the cross-domain scripting problem. The expression of digital rights using JSON, however, represents a novel approach, bypassing the need for language processing, and in addition, solving the cross-domain scripting problem.

Privacy preserving ID3 using Gini Index over horizontally partitioned data

2008 IEEE/ACS International Conference on Computer Systems and Applications, 2008

The ID3 algorithm is a standard, popular, and simple method for data classification and decision ... more The ID3 algorithm is a standard, popular, and simple method for data classification and decision tree creation. Since privacy-preserving data mining should be taken into consideration, several secure multi-party computation protocols have been presented based on this technique. Entropy and Gini Index are two protocols which compute Information-Gain at each step when producing a decision tree. The Gini Index, however, has been less studied in privacy-preserving data mining protocols. In this paper, we show how Gini can be used in privacy-preserving ID3 algorithms to create decision tree classifications in such a way that involved parties can jointly compute the gain value of each normal attribute without revealing their own private information to each other, while the database is horizontally partitioned over two or more parties. Three secure multiparty sub-protocols are presented to evaluate the intermediate computations. The communication overhead has been kept reasonably low to make the whole protocol efficient and practical.

New Incremental Privacy-Preserving Clustering Protocols

Lecture Notes on Software Engineering, 2013

ABSTRACT We consider the problem of data clustering on streamed data, when the number of transact... more ABSTRACT We consider the problem of data clustering on streamed data, when the number of transactions is growing very quickly, or when data is distributed among several parties and their privacy is a concern. In this paper we present two new protocols for incremental privacy-preserving k-means clustering, which is a very popular data mining method, when data is distributed, horizontally or vertically, among multiple parties. At the end of each protocol, each party, without revealing its own private data, receives the final result of the clustering algorithm. Also, to improve efficiency, previous knowledge is used to incrementally update the centers and membership of each cluster.

A Protocol for the Secure Linking of Registries for HPV Surveillance

PLoS ONE, 2012

Introduction: In order to monitor the effectiveness of HPV vaccination in Canada the linkage of m... more Introduction: In order to monitor the effectiveness of HPV vaccination in Canada the linkage of multiple data registries may be required. These registries may not always be managed by the same organization and, furthermore, privacy legislation or practices may restrict any data linkages of records that can actually be done among registries. The objective of this study was to develop a secure protocol for linking data from different registries and to allow on-going monitoring of HPV vaccine effectiveness.

A secure protocol for protecting the identity of providers when disclosing data for disease surveillance

Journal of the American Medical Informatics Association, 2011

A secure distributed logistic regression protocol for the detection of rare adverse drug events

Journal of the American Medical Informatics Association, 2013

There is limited capacity to assess the comparative risks of medications after they enter the mar... more There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models.

Privacy-Preserving Classification and Clustering Using Secure Multi-Party Computation

Proceeding of The International Conference on …

Nowadays, data mining and machine learning techniques are widely used in electronic applications ... more Nowadays, data mining and machine learning techniques are widely used in electronic applications in different areas such as e-government, e-health, e-business, and so on. One major and very crucial issue in these type of systems, which are normally distributed among two or more parties and are dealing with sensitive data, is preserving the privacy of individual's sensitive information. Each party wants to keep its own raw data private while getting useful knowledge from the whole data owned by all the parties. Privacy-preserving Data Mining is dealing with this problem and many protocols have been introduced for various standard data mining algorithms so far. In this paper, we propose some new protocols for two popular techniques, classification and clustering, when data is horizontally or vertically partitioned among two or more parties. In classification we use Gini Index applying on ID3 algorithm to create decision tree from distributed data. We also propose two protocols for k-means Clustering which is a prominent clustering algorithm in data mining techniques. Some secure two and multi-party building blocks such as secure comparison, secure multi-party addition and multiplication are also proposed to use as sub-protocols in our algorithms.

Privacy-preserving back-propagation and extreme learning machine algorithms

Data & Knowledge Engineering, 2012

ABSTRACT Neural network systems are highly capable of deriving knowledge from complex data, and t... more ABSTRACT Neural network systems are highly capable of deriving knowledge from complex data, and they are used to extract patterns and trends which are otherwise hidden in many applications. Preserving the privacy of sensitive data and individuals&#39; information is a major challenge in many of these applications. One of the most popular algorithms in neural network learning systems is the back-propagation (BP) algorithm, which is designed for single-layer and multi-layer models and can be applied to continuous data and differentiable activation functions. Another recently introduced learning technique is the extreme learning machine (ELM) algorithm. Although it works only on single-layer models, ELM can out-perform the BP algorithm by reducing the communication required between parties in the learning phase. In this paper, we present new privacy-preserving protocols for both the BP and ELM algorithms when data is horizontally and vertically partitioned among several parties. These new protocols, which preserve the privacy of both the input data and the constructed learning model, can be applied to online incoming records and/or batch learning. Furthermore, the final model is securely shared among all parties, who can use it jointly to predict the corresponding output for their target data.

Privacy Preserving K-Means Clustering In Multi-Party Environment

Proceedings of the International Conference on …, 2007

Extracting meaningful and valuable knowledge from databases is often done by various data mining ... more Extracting meaningful and valuable knowledge from databases is often done by various data mining algorithms. Nowadays, databases are distributed among two or more parties because of different reasons such as physical and geographical restrictions and the most important issue is privacy. Related data is normally maintained by more than one organization, each of which wants to keep its individual information private. Thus, privacy-preserving techniques and protocols are designed to perform data mining on distributed environments when privacy is highly concerned. Cluster analysis is a technique in data mining, by which data can be divided into some meaningful clusters, and it has an important role in different fields such as bio-informatics, marketing, machine learning, climate and medicine. k-means Clustering is a prominent algorithm in this category which creates a one-level clustering of data. In this paper we introduce privacy-preserving protocols for this algorithm, along with a protocol for Secure comparison, known as the Millionaires' Problem, as a sub-protocol, to handle the clustering of horizontally or vertically partitioned data among two or more parties.

Managing digital rights using JSON

by Stephen Downes, Saeed Samet, and Rodrigue Savoie

Abstract Prior art in the expression of digital rights using XML is demonstrated to require a pro... more Abstract Prior art in the expression of digital rights using XML is demonstrated to require a process of interpretation or parsing, as is characteristic with language processing, and in addition to be subject to the cross-domain scripting problem. The expression of digital rights using JSON, however, represents a novel approach, bypassing the need for language processing, and in addition, solving the cross-domain scripting problem.

A secure revocable personal health record system with policy-based fine-grained access control

2015 13th Annual Conference on Privacy, Security and Trust (PST), 2015

SNACK: An efficient intrusion detection system in Mobile Ad-Hoc Network based on the Selective-Negative Acknowledgement algorithm

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), 2015

Privacy-Preserving Classification and Clustering Using Secure Multi-Party Computation

Nowadays, data mining and machine learning techniques are widely used in electronic applications ... more Nowadays, data mining and machine learning techniques are widely used in electronic applications in different areas such as e-government, e-health, e-business, and so on. One major and very crucial issue in these type of systems, which are normally distributed among two or more parties and are dealing with sensitive data, is preserving the privacy of individual’s sensitive information. Each

Privacy Preserving k-Means Clustering in Multi-Party Environment

Secrypt, 2007

Extracting meaningful and valuable knowledge from databases is often done by various data mining ... more Extracting meaningful and valuable knowledge from databases is often done by various data mining algorithms. Nowadays, databases are distributed among two or more parties because of different reasons such as physical and geographical restrictions and the most important issue is privacy. Related data is normally maintained by more than one organization, each of which wants to keep its individual information private. Thus, privacy-preserving techniques and protocols are designed to perform data mining on distributed environments when privacy is highly concerned. Cluster analysis is a technique in data mining, by which data can be divided into some meaningful clusters, and it has an important role in different fields such as bio-informatics, marketing, machine learning, climate and medicine. k-means Clustering is a prominent algorithm in this category which creates a one-level clustering of data. In this paper we introduce privacy-preserving protocols for this algorithm, along with a protocol for Secure comparison, known as the Millionaires' Problem, as a sub-protocol, to handle the clustering of horizontally or vertically partitioned data among two or more parties.

SUFFUSE: Simultaneous Fuzzy-Rough Feature-Sample Selection

Journal of Advances in Information Technology, 2015

Privacy-Preserving Logistic Regression

Journal of Advances in Information Technology, 2015

A privacy preserving protocol for tracking participants in phase I clinical trials

Journal of Biomedical Informatics, 2015

Some phase 1 clinical trials offer strong financial incentives for healthy individuals to partici... more Some phase 1 clinical trials offer strong financial incentives for healthy individuals to participate in their studies. There is evidence that some individuals enroll in multiple trials concurrently. This creates safety risks and introduces data quality problems into the trials. Our objective was to construct a privacy preserving protocol to track phase 1 participants to detect concurrent enrollment. A protocol using secure probabilistic querying against a database of trial participants that allows for screening during telephone interviews and on-site enrollment was developed. The match variables consisted of demographic information. The accuracy (sensitivity, precision, and negative predictive value) of the matching and its computational performance in seconds were measured under simulated environments. Accuracy was also compared to non-secure matching methods. The protocol performance scales linearly with the database size. At the largest database size of 20,000 participants, a query takes under 20s on a 64 cores machine. Sensitivity, precision, and negative predictive value of the queries were consistently at or above 0.9, and were very similar to non-secure versions of the protocol. The protocol provides a reasonable solution to the concurrent enrollment problems in phase 1 clinical trials, and is able to ensure that personal information about participants is kept secure.

Privacy-preserving protocols for perceptron learning algorithm in neural networks

2008 4th International IEEE Conference Intelligent Systems, 2008

Neural networks have become increasingly important in areas such as medical diagnosis, bio-inform... more Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy of individual's private information and sensitive data. In this paper, we propose two secure protocols for perceptron learning algorithm when input data is horizontally and vertically partitioned among the parties. These protocols can be applied in both linearly separable and non-separable datasets, while not only data belonging to each party remains private, but the final learning model is also securely shared among those parties. Parties then can jointly and securely apply the constructed model to predict the output corresponding to their target data. Also, these protocols can be used incrementally, i.e. they process new coming data, adjusting the previously constructed network.

Secure two and multi-party association rule mining

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009

Abstract Association rule mining provides useful knowledge from raw data in different application... more

Secure Surveillance of Antimicrobial Resistant Organism Colonization or Infection in Ontario Long Term Care Homes

PLoS ONE, 2014

Background: There is stigma attached to the identification of residents carrying antimicrobial re... more

Managing Digital Rights Using JSON

2010 7th IEEE Consumer Communications and Networking Conference, 2010

Prior art in the expression of digital rights using XML is demonstrated to require a process of i... more Prior art in the expression of digital rights using XML is demonstrated to require a process of interpretation or parsing, as is characteristic with language processing, and in addition to be subject to the cross-domain scripting problem. The expression of digital rights using JSON, however, represents a novel approach, bypassing the need for language processing, and in addition, solving the cross-domain scripting problem.

Privacy preserving ID3 using Gini Index over horizontally partitioned data

2008 IEEE/ACS International Conference on Computer Systems and Applications, 2008

The ID3 algorithm is a standard, popular, and simple method for data classification and decision ... more The ID3 algorithm is a standard, popular, and simple method for data classification and decision tree creation. Since privacy-preserving data mining should be taken into consideration, several secure multi-party computation protocols have been presented based on this technique. Entropy and Gini Index are two protocols which compute Information-Gain at each step when producing a decision tree. The Gini Index, however, has been less studied in privacy-preserving data mining protocols. In this paper, we show how Gini can be used in privacy-preserving ID3 algorithms to create decision tree classifications in such a way that involved parties can jointly compute the gain value of each normal attribute without revealing their own private information to each other, while the database is horizontally partitioned over two or more parties. Three secure multiparty sub-protocols are presented to evaluate the intermediate computations. The communication overhead has been kept reasonably low to make the whole protocol efficient and practical.

New Incremental Privacy-Preserving Clustering Protocols

Lecture Notes on Software Engineering, 2013

ABSTRACT We consider the problem of data clustering on streamed data, when the number of transact... more ABSTRACT We consider the problem of data clustering on streamed data, when the number of transactions is growing very quickly, or when data is distributed among several parties and their privacy is a concern. In this paper we present two new protocols for incremental privacy-preserving k-means clustering, which is a very popular data mining method, when data is distributed, horizontally or vertically, among multiple parties. At the end of each protocol, each party, without revealing its own private data, receives the final result of the clustering algorithm. Also, to improve efficiency, previous knowledge is used to incrementally update the centers and membership of each cluster.

A Protocol for the Secure Linking of Registries for HPV Surveillance

PLoS ONE, 2012

Introduction: In order to monitor the effectiveness of HPV vaccination in Canada the linkage of m... more Introduction: In order to monitor the effectiveness of HPV vaccination in Canada the linkage of multiple data registries may be required. These registries may not always be managed by the same organization and, furthermore, privacy legislation or practices may restrict any data linkages of records that can actually be done among registries. The objective of this study was to develop a secure protocol for linking data from different registries and to allow on-going monitoring of HPV vaccine effectiveness.

A secure protocol for protecting the identity of providers when disclosing data for disease surveillance

Journal of the American Medical Informatics Association, 2011

A secure distributed logistic regression protocol for the detection of rare adverse drug events

Journal of the American Medical Informatics Association, 2013

There is limited capacity to assess the comparative risks of medications after they enter the mar... more There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models.

Privacy-Preserving Classification and Clustering Using Secure Multi-Party Computation

Proceeding of The International Conference on …

Nowadays, data mining and machine learning techniques are widely used in electronic applications ... more Nowadays, data mining and machine learning techniques are widely used in electronic applications in different areas such as e-government, e-health, e-business, and so on. One major and very crucial issue in these type of systems, which are normally distributed among two or more parties and are dealing with sensitive data, is preserving the privacy of individual's sensitive information. Each party wants to keep its own raw data private while getting useful knowledge from the whole data owned by all the parties. Privacy-preserving Data Mining is dealing with this problem and many protocols have been introduced for various standard data mining algorithms so far. In this paper, we propose some new protocols for two popular techniques, classification and clustering, when data is horizontally or vertically partitioned among two or more parties. In classification we use Gini Index applying on ID3 algorithm to create decision tree from distributed data. We also propose two protocols for k-means Clustering which is a prominent clustering algorithm in data mining techniques. Some secure two and multi-party building blocks such as secure comparison, secure multi-party addition and multiplication are also proposed to use as sub-protocols in our algorithms.

Privacy-preserving back-propagation and extreme learning machine algorithms

Data & Knowledge Engineering, 2012

ABSTRACT Neural network systems are highly capable of deriving knowledge from complex data, and t... more ABSTRACT Neural network systems are highly capable of deriving knowledge from complex data, and they are used to extract patterns and trends which are otherwise hidden in many applications. Preserving the privacy of sensitive data and individuals&#39; information is a major challenge in many of these applications. One of the most popular algorithms in neural network learning systems is the back-propagation (BP) algorithm, which is designed for single-layer and multi-layer models and can be applied to continuous data and differentiable activation functions. Another recently introduced learning technique is the extreme learning machine (ELM) algorithm. Although it works only on single-layer models, ELM can out-perform the BP algorithm by reducing the communication required between parties in the learning phase. In this paper, we present new privacy-preserving protocols for both the BP and ELM algorithms when data is horizontally and vertically partitioned among several parties. These new protocols, which preserve the privacy of both the input data and the constructed learning model, can be applied to online incoming records and/or batch learning. Furthermore, the final model is securely shared among all parties, who can use it jointly to predict the corresponding output for their target data.

Privacy Preserving K-Means Clustering In Multi-Party Environment

Proceedings of the International Conference on …, 2007

Extracting meaningful and valuable knowledge from databases is often done by various data mining ... more Extracting meaningful and valuable knowledge from databases is often done by various data mining algorithms. Nowadays, databases are distributed among two or more parties because of different reasons such as physical and geographical restrictions and the most important issue is privacy. Related data is normally maintained by more than one organization, each of which wants to keep its individual information private. Thus, privacy-preserving techniques and protocols are designed to perform data mining on distributed environments when privacy is highly concerned. Cluster analysis is a technique in data mining, by which data can be divided into some meaningful clusters, and it has an important role in different fields such as bio-informatics, marketing, machine learning, climate and medicine. k-means Clustering is a prominent algorithm in this category which creates a one-level clustering of data. In this paper we introduce privacy-preserving protocols for this algorithm, along with a protocol for Secure comparison, known as the Millionaires' Problem, as a sub-protocol, to handle the clustering of horizontally or vertically partitioned data among two or more parties.