Survey and Analysis of Cryptographic Techniques for Privacy Protection in Recommender Systems

Ogunseyi, Taiwo Blessing; Yang, Cheng

doi:10.1007/978-3-030-00012-7_63

Cited by 7 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using this concept, the general form of the string is “privacy” AND “security” AND “mobile” AND “(app OR application)” AND “(recommendation OR recommender)”. In the same manner, we wanted to classify studies according to data obfuscation and cryptographic techniques (Ogunseyi and Yang, 2018). So, instead of creating a research question, we have adopted the systematic mapping approach as discussed in Enríquez et al (2019).…”

Section: Slr Process and Resultsmentioning

confidence: 99%

Data usage-based privacy and security issues in mobile app recommendation (MAR): a systematic literature review

Beg

Khan

Anjum

2021

LHT

View full text Add to dashboard Cite

PurposeSimilarly, Zhu et al. (2014) and Zhang et al. (2014) stated that addressing privacy concerns with the recommendation process is necessary for the healthy development of app recommendation. Recently, Xiao et al. (2020) mentioned that a lack of effective privacy policy hinders the development of personalized recommendation services. According to the reported work, privacy protection technology methods are too limited for mobile focusing on data encryption, anonymity, disturbance, elimination of redundant data to protect the recommendation process from privacy breaches. So, this situation motivated us to conduct a systematic literature review (SLR) to provide the viewpoint of privacy and security concerns as mentioned in current state-of-the-art in the mobile app recommendation domain.Design/methodology/approachIn this work, the authors have followed Kitchenham guidelines (Kitchenham and Charters, 2007) to devise the SLR process. According to the guidelines, the SLR process has three main phases: (1) define, (2) conduct the search and (3) report the results. Furthermore, the authors used systematic mapping approach as well to ensure the whole process.FindingsBased on the selected studies, the authors proposed three main thematic taxonomies, including architectural style, security and privacy strategies, and user-usage in the mobile app recommendation domain. From the studies' synthesis viewpoint, it is observed that the majority of the research efforts have focused on the movie recommendation field, while the mainly used privacy scheme is homomorphic encryption. Finally, the authors suggested a set of future research dimensions useful for the potential researchers interested to perform the research in the mobile app recommendation domain.Originality/valueThis is an SLR article, based on existing published research, where the authors identified key issues and future directions.

show abstract

Section: Slr Process and Resultsmentioning

confidence: 99%

Data usage-based privacy and security issues in mobile app recommendation (MAR): a systematic literature review

Beg

Khan

Anjum

2021

LHT

View full text Add to dashboard Cite

show abstract

“…Secret sharing as a cryptographic tool can be applied in a situation where access to sensitive information has to be protected by more than one party. It is a scheme in which different shares of a secret are distributed to parties such that only a fixed subset of parties can reconstruct the secret [60]. Secret sharing may be used to preserve privacy in digital forensics by distributing a secret (suspect's data) into n pieces/data files and storing them in different locations to prevent leaking.…”

Section: Secret Sharingmentioning

confidence: 99%

Cryptographic Techniques for Data Privacy in Digital Forensics

Ogunseyi,

Adedayo

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

The acquisition and analysis of data in digital forensics raise different data privacy challenges. Many existing works on digital forensic readiness discuss what information should be stored and how to collect relevant data to facilitate investigations. However, the cost of this readiness often directly impacts the privacy of innocent third parties and suspects if the collected information is irrelevant. Approaches that have been suggested for privacy-preserving digital forensics focus on the use of policy, non-cryptography-based, and cryptography-based solutions. Cryptographic techniques have been proposed to address issues of data privacy during data analysis. As the utilization of some of these cryptographic techniques continues to increase, it is important to evaluate their applicability and challenges in relation to digital forensics processes. This study provides digital forensics investigators and researchers with a roadmap to understanding the data privacy challenges in digital forensics and examines the various privacy techniques that can be utilized to tackle these challenges. Specifically, we review the cryptographic techniques applied for privacy protection in digital forensics and categorize them within the context of whether they support trusted third parties, multiple investigators, and multi-keyword searches. We highlight some of the drawbacks of utilizing cryptography-based methods in privacy-preserving digital forensics and suggest potential solutions to the identified shortcomings. In addition, we propose a conceptual privacypreserving digital forensics (PPDF) model that is based on the use of cryptographic techniques and analyze the model within the context of the above-mentioned factors. An evaluation of the model is provided through a consideration of identified factors that may affect an investigation. Lastly, we provide an analysis of how existing principles for preserving privacy in digital forensics are addressed in our PPDF model. Our evaluation shows that the model aligns with many of the existing privacy principles recommended for privacy protection in digital forensics.

show abstract

“…Recent years, Recommender System helps to solve the problem of information overload while providing personalized information retrieval [14]. However, in order to improve the recommendation efficiency, the system requires the personal information of users, which is a serious privacy concern for many users [15,16]. Recent research indicates that there are two methods to solve the privacy problem of the Recommender system: Architecture-based and Algorithms-based [17].…”

Section: Related Workmentioning

confidence: 99%

“…end (10) forAgen k ∈ AC i do (11) calculate W aggr,k ′ following equation ( 13); ( 12) end (13) calculate W rec ′ � W fe d ′ following equation ( 14); ( 14) end (15) our scheme takes the least time no matter how many Items. is is because the encryption form in PRO-NBR can only deal with a bit message per encryption, and EPRT can deal with an integer message, whereas our scheme can deal with p integer messages.…”

Section: Efficiency Comparisonmentioning

confidence: 99%

Efficient Personalized Recommendation Based on Federated Learning with Similarity Ciphertext Calculation

Wang

Huang

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

With the popularity of big data, people get less useful information because of the large amount of data, which makes the Recommender System come into being. However, the privacy and accuracy of the Recommender System still have great challenges. To address these challenges, an efficient personalized recommendation scheme is proposed based on Federated Learning with similarity ciphertext calculation. In this paper, we first design a Similarity calculation algorithm based on Orthogonal Matrix in Ciphertext (SOMC), which can compute the Similarity between users’ demand and Items’ attributes under ciphertext with a low calculation cost. Based on SOMC, we construct an efficient recommendation scheme by employing the Federated Learning framework. The important feature of the proposed approach is improving the accuracy of recommendation while ensuring the privacy of both the users and the Agents. Furthermore, the Agents with good performance are selected according to their Reliability scores to participate in the federal recommendation, so as to further make the accuracy of recommendation better. Under the defined threat model, it is proved that the proposed scheme can meet the privacy requirements of users and Agents. Experiments show that the proposed scheme has optimized accuracy and efficiency compared with existing schemes.

show abstract

Survey and Analysis of Cryptographic Techniques for Privacy Protection in Recommender Systems

Cited by 7 publications

References 40 publications

Data usage-based privacy and security issues in mobile app recommendation (MAR): a systematic literature review

Data usage-based privacy and security issues in mobile app recommendation (MAR): a systematic literature review

Cryptographic Techniques for Data Privacy in Digital Forensics

Efficient Personalized Recommendation Based on Federated Learning with Similarity Ciphertext Calculation

Contact Info

Product

Resources

About