This paper addresses the security of private scalar product protocols used in privacy-preserving data mining. It shows that two protocols, one proposed in a leading data mining conference and another recently proposed, are insecure. The paper then presents a provably private scalar product protocol based on homomorphic encryption, which is more efficient and can be used on large datasets. The paper begins by discussing the importance of secure scalar product protocols in privacy-preserving data mining. It then describes the Vaidya-Clifton protocol, which is used for computing frequent itemsets from vertically partitioned transaction databases. The protocol involves computing the scalar product of binary vectors representing the transactions. However, the paper shows that this protocol is insecure, as it allows one party to learn the private input of the other party with high probability. The paper also analyzes the Du-Atallah protocol, which is another private scalar product protocol. It shows that this protocol is also insecure, as it allows one party to learn the private input of the other party with high probability. The paper then presents a new private scalar product protocol based on homomorphic encryption. This protocol is proven to be secure under standard cryptographic assumptions. It is also more efficient than previous protocols, as it can be used on large datasets. The paper concludes that the Vaidya-Clifton and Du-Atallah protocols are insecure and that the new protocol is a more secure and efficient alternative. The new protocol is based on homomorphic encryption and is proven to be secure under standard cryptographic assumptions. It is also more efficient than previous protocols, as it can be used on large datasets.This paper addresses the security of private scalar product protocols used in privacy-preserving data mining. It shows that two protocols, one proposed in a leading data mining conference and another recently proposed, are insecure. The paper then presents a provably private scalar product protocol based on homomorphic encryption, which is more efficient and can be used on large datasets. The paper begins by discussing the importance of secure scalar product protocols in privacy-preserving data mining. It then describes the Vaidya-Clifton protocol, which is used for computing frequent itemsets from vertically partitioned transaction databases. The protocol involves computing the scalar product of binary vectors representing the transactions. However, the paper shows that this protocol is insecure, as it allows one party to learn the private input of the other party with high probability. The paper also analyzes the Du-Atallah protocol, which is another private scalar product protocol. It shows that this protocol is also insecure, as it allows one party to learn the private input of the other party with high probability. The paper then presents a new private scalar product protocol based on homomorphic encryption. This protocol is proven to be secure under standard cryptographic assumptions. It is also more efficient than previous protocols, as it can be used on large datasets. The paper concludes that the Vaidya-Clifton and Du-Atallah protocols are insecure and that the new protocol is a more secure and efficient alternative. The new protocol is based on homomorphic encryption and is proven to be secure under standard cryptographic assumptions. It is also more efficient than previous protocols, as it can be used on large datasets.