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Location-based services (LBSs) provide appropriate information based on users’ locations. These services can be invoked by an individual user or a group of users. Using these services requires users to reveal their locations; thus, providing uses’ location privacy during the use of these services is an important issue. There are many works to protect users’ location privacy. In this paper, we have reviewed the related works to provide the location privacy for a group of users during the use of LBSs. We have classified them into two categories: the first category consists of the solutions that protect an individual user location privacy through group formation, while the second category contains the specific solutions to provide group location privacy. We have then analyzed and compared the performance and security properties of the related works, and have identified the open issues and future works in this field.

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Doing a joint and secure computation on private inputs (Secure Multiparty Computation) is an interesting problem in the field of information security. The Millionaire problem is the first SMC problem in which two millionaires wish to know who is richer without disclosing their wealth. Then many problems have been defined in the field of secure multiparty computation. In this paper, the problem of secure multiparty summation is considered where a group of users wants to jointly and securely compute the summation value of their private inputs. We have reviewed and compared the related works in this filed; we have also identified the open issues and future works.