Lựa chọn tính năng bằng cách sử dụng bảo vệ quyền riêng tư của các hệ thống gợi ý dựa trên lọc hợp tác và sự tin cậy lẫn nhau trong mạng xã hội

Alatas B, Akin E, Ozer AB (2009) Chaos embedded particle swarm optimization algorithms. Chaos, Solitons Fractals 40(4):1715–1734 Alligood KT, Sauer TD, Yorke JA Chaos (1996) Springer, New York, pp 105–147 Amini S, Homayouni S, Safari A et al (2018) Object-based classification of hyperspectral data using Random Forest algorithm. Geo Spatial Inf Sci 21(2):127–138 Banati H, Mehta M, Bajaj M (2014) Social behaviour based metrics to enhance collaborative filtering. Int J Comput Inf Syst Ind Manage Appl 6:217–226 Bertino E, Fovino IN, Provenza LP (2005) A framework for evaluating privacy preserving data mining algorithms. Data Min Knowl Discov 11(2):121–154 Bilge A, Polat H (2013) A scalable privacy-preserving recommendation scheme via bisecting k-means clustering. Inf Process Manage 49(4):912–927 Bobadilla J et al (2013) Recommender systems survey. Knowl Based Syst 46:109–132 Canny J (2002) Collaborative filtering with privacy. In: 2002 IEEE symposium on security and privacy, 2002. Proceedings. IEEE Cao J et al (2013) Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web 16(5–6):729–748 Casino F et al (2015) A k-anonymous approach to privacy preserving collaborative filtering. J Comput Syst Sci 81(6):1000–1011 Chakraborty P, Karforma S (2013) Detection of Profile-injection attacks in recommender systems using outlier analysis. Procedia Technol 10:963–969 Chang Z, Cao J, Zhang Y (2018) A novel image segmentation approach for wood plate surface defect classification through convex optimization. J For Res 29(6):1789–1795 Chen S, Lu R, Zhang J (2017) A flexible privacy-preserving framework for singular value decomposition under internet of things environment. IFIP International conference on trust management. Springer, Cham Chiou S-Y (2017) A trustworthy online recommendation system based on social connections in a privacy-preserving manner. Multimed Tools Appl 76(7):9319–9336 Dou K, Guo B, Kuang L (2019) A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection. Multimed Tools Appl 78:26907–26926 Dua S, Du X (2016) Data mining and machine learning in cybersecurity. Auerbach Publications, Boca Raton Erkin Z et al (2012) Generating private recommendations efficiently using homomorphic encryption and data packing. IEEE Trans Inf Forensics Sec 7(3):1053–1066 Fayyoumi E, Oommen BJ (2010) A survey on statistical disclosure control and micro-aggregation techniques for secure statistical databases. Softw Pract Exp 40(12):1161–1188 Georgiadis CK et al (2017) A method for privacy-preserving collaborative filtering recommendations. J Univ Comput Sci 23(2):146–166 Golbeck JA (2005) Computing and applying trust in web-based social networks. Diss Gong S (2011) Privacy-preserving collaborative filtering based on randomized perturbation techniques and secure multiparty computation. Int J Adv Comput Technol 3(4):89–99 Goyal N, Aggarwal N, Dutta M (2015) A novel way of assigning software bug priority using supervised classification on clustered bugs data. In: Advances in intelligent informatics. Springer, Cham, pp 493–501 Gunes I et al (2014) Shilling attacks against recommender systems: a comprehensive survey. Artif Intell Rev 42(4):767–799 Guo L, Zhang C, Fang Y (2015) A trust-based privacy-preserving friend recommendation scheme for online social networks. IEEE Trans Dependable Secure Comput 12(4):413–427 Hamidzadeh J, Namaei N (2019) Belief-based chaotic algorithm for support vector data description. Soft Comput 23:4289–4314 Hamidzadeh J, Sadeghi R, Namaei N (2017) Weighted support vector data description based on chaotic bat algorithm. Appl Soft Comput 60:540–551 Heidari S et al (2019) Big data clustering with varied density based on MapReduce. J Big Data 6(1):77 Huber PJ (2011) Robust statistics. In: International encyclopedia of statistical science. Springer, Berlin, pp 1248–1251 Izakian H, Abraham A (2011) Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst Appl 38(3):1835–1838 Jain N, Singh A (2017) A survey on privacy preserving mining various techniques with attacks Ji P, Zhang H-Y, Wang J-Q (2019) A fuzzy decision support model with sentiment analysis for items comparison in e-commerce: the case study of PConline. com. IEEE Trans Syst Man Cybern Syst 49(10):1993–2004. https://doi.org/10.1109/TSMC.2018.2875163 Jian-min H et al (2008) A complete (alpha, k)-anonymity model for sensitive values individuation preservation. In: 2008 International symposium on electronic commerce and security. IEEE Kaur H, Kumar N, Batra S (2018) An efficient multi-party scheme for privacy preserving collaborative filtering for healthcare recommender system. Future Gener Comput Syst 86:297–307 Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, Boston, pp 760–766 Kikuchi H, Mochizuki A (2013) Privacy-preserving collaborative filtering using randomized response. J Inf Process 21(4):617–623 Lam S, Frankowski D, Riedl J (2006) Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In: Emerging trends in information and communication security, pp 14–29 Leski JM (2016) Fuzzy c-ordered-means clustering. Fuzzy Sets Syst 286:114–133 Li C, Luo Z (2011) A hybrid Item-based recommendation algorithm against segment attack in collaborative filtering systems. In: 2011 International conference on information management, innovation management and industrial engineering (ICIII), vol 2. IEEE Li D et al (2015) An algorithm for efficient privacy-preserving item-based collaborative filtering. Future Gener Comput Syst 55:311–320 Li D et al (2016) An algorithm for efficient privacy-preserving item-based collaborative filtering. Future Gener Comput Syst 55:311–320 Li J et al (2017) Enforcing differential privacy for shared collaborative filtering. IEEE Access 5:35–49 Liang R, Wang J (2019) A linguistic intuitionistic cloud decision support model with sentiment analysis for product selection in E-commerce. Int J Fuzzy Syst 21(3):963–977 Liang W et al (2019) TBRS: a trust based recommendation scheme for vehicular CPS network. Future Gener Comput Syst 92:383–398 Liu X et al (2017) When differential privacy meets randomized perturbation: a hybrid approach for privacy-preserving recommender system. In: International conference on database systems for advanced applications. Springer, Cham Long Q, Hu Q (2010) Robust evaluation of binary collaborative recommendation under profile injection attack. In: 2010 IEEE international conference on progress in informatics and computing (PIC), vol 2. IEEE Ma X et al (2017) APPLET: a privacy-preserving framework for location-aware recommender system. Sci China Inf Sci 60(9):092101 Ma X et al (2018) ARMOR: A trust-based privacy-preserving framework for decentralized friend recommendation in online social networks. Future Gener Comput Syst 79:82–94 Martinez-Balleste A et al (2007) A genetic approach to multivariate microaggregation for database privacy. In: 2007 IEEE 23rd International conference on data engineering workshop. IEEE Mol G, John S (2017) A trustworthy model in E-commerce by mining feedback comments Patil K, Jadhav N (2017) Multi-layer perceptron classifier and Paillier encryption scheme for friend recommendation system. In: 2017 International conference on computing, communication, control and automation (ICCUBEA). IEEE Polatidis N et al (2017) Privacy-preserving collaborative recommendations based on random perturbations. Expert Syst Appl 71:18–25 Rebollo-Monedero D et al (2017) p-Probabilistic k-anonymous microaggregation for the anonymization of surveys with uncertain participation. Inf Sci 382:388–414 Sadeghi R, Hamidzadeh J (2018) Automatic support vector data description. Soft Comput 22(1):147–158 Shieh J-R (2015) An end-to-end encrypted domain proximity recommendation system using secret sharing homomorphic cryptography. In: 2015 International Carnahan Conference on Security Technology (ICCST). IEEE Silva Filho TM et al (2015) Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Syst Appl 42(17–18):6315–6328 Soni K, Panchal G (2017) Data security in recommendation system using homomorphic encryption. In: International conference on information and communication technology for intelligent systems. Springer, Cham Storch LS et al (2017) Revisiting the logistic map: a closer look at the dynamics of a classic chaotic population model with ecologically realistic spatial structure and dispersal. Theor Popul Biol 114:10–18 Strogatz SH (2018) Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. CRC Press, Boca Raton Templ M, Meindl B, Kowarik A (2013) Introduction to statistical disclosure control (SDC). In: Project: relative to the testing of SDC algorithms and provision of practical SDC, data analysis OG Wang L et al (2020) The differences in hotel selection among various types of travellers: a comparative analysis with a useful bounded rationality behavioural decision support model. Tour Manag 76:103961 Xiong H et al (2017) A novel recommendation algorithm frame for tourist Spots based on multi-clustering bipartite graphs. In: 2017 IEEE 2nd international conference on cloud computing and big data analysis (ICCCBDA). IEEE Xiong P et al (2018) Private collaborative filtering under untrusted recommender server. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2018.05.077 Yang B, Lei Yu, Liu J, Li W (2017) Social collaborative filtering by trust. IEEE Trans Pattern Anal Mach Intell 39(8):1633–1647 Yi X, Zhang Y (2009) Privacy-preserving naive Bayes classification on distributed data via semi-trusted mixers. Inf Syst 34(3):371–380 You H et al (2015) An improved collaborative filtering recommendation algorithm combining item clustering and Slope One scheme. In: Proceedings of the international multi-conference of engineers and computer scientists, vol 1 Zhan J et al (2010) Privacy-preserving collaborative recommender systems. IEEE Trans Syst Man Cybern Part C (Appl Rev) 40(4):472–476 Zhang F (2009) Average shilling attack against trust-based recommender systems. In: 2009 International conference on information management, innovation management and industrial engineering, vol 4. IEEE Zhang F (2010) Analysis of love-hate shilling attack against e-commerce recommender system. In: 2010 International conference of information science and management engineering (ISME), vol 1. IEEE Zhang J, Zhu J, Zhang N (2014) An improved privacy-preserving collaborative filtering recommendation algorithm. In: PACIS Zhu T et al (2014) Privacy preserving collaborative filtering for KNN attack resisting. Soc Netw Anal Min 4(1):196