Toward Privacy Protection for Location based Recommender Systems: A Survey of the state-of-the-art

Alnazzawi, Tahani; Alotaibi, Reem; Hamza, Nermin

doi:10.1109/cais.2018.8441958

Cited by 3 publications

(1 citation statement)

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“…They proposed a new framework to support private queries and evaluations. Al-Nazzawi et al's study [15] is a summary of the current research status based on LBRS. This paper explores the standards of privacy protection.…”

Section: Recommendation Systems Based On Privacy Protectionmentioning

confidence: 99%

SDRM‐LDP: A Recommendation Model Based on Local Differential Privacy

Yin

Yang

et al. 2021

Wireless Communications and Mobile Computing

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The development of 5G technology has driven the rise of e-commerce, social networking, and the Internet of Things. Under the high-speed transmission, the data volume increases, and the user demand also changes. Personalized customization has become the mainstream trend of network development. However, as the speed of the Internet increases, a series of problems also arise. The increase in data volume results in a reduction of bandwidth, a growth of the central processor’s pressure, and a higher risk of data leakage. A search system and a recommendation platform are the tools to improve people’s search efficiency. However, providing personalized recommendations to different users according to their needs is still an urgent problem. Simultaneously, the big data volume means that attackers can also get more information. They can use background knowledge and various reasoning methods to deduce the user’s private information using nonprivate items. In this paper, the solutions to safe and reliable recommendation services are the main problem explored. Based on this idea, this paper proposed short-term dynamic recommendation model based on local differential privacy (SDRM-LDP). This model uses a small amount of user information to construct short-term user preference behaviors and provides recommendations for users based on the similarity between items. We consider that an attacker uses nonprivate items to derive privacy items. Therefore, we randomly replace the original data in the same category. At the same time, the local differential privacy (LDP) is added to the privacy item query to make the private data available and protect the privacy information. In this paper, two real-world datasets, ML-100K and ML-10M, are used for experiments. Experimental results show that the results of SDRM-LDP are superior to other models.

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Section: Recommendation Systems Based On Privacy Protectionmentioning

confidence: 99%