Letteratura scientifica selezionata sul tema "Online Inference Attacks"
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Articoli di riviste sul tema "Online Inference Attacks":
Gong, Neil Zhenqiang, e Bin Liu. "Attribute Inference Attacks in Online Social Networks". ACM Transactions on Privacy and Security 21, n. 1 (6 gennaio 2018): 1–30. http://dx.doi.org/10.1145/3154793.
Lachat, Paul, Nadia Bennani, Veronika Rehn-Sonigo, Lionel Brunie e Harald Kosch. "Detecting Inference Attacks Involving Raw Sensor Data: A Case Study". Sensors 22, n. 21 (24 ottobre 2022): 8140. http://dx.doi.org/10.3390/s22218140.
Nithish Ranjan Gowda, Et al. "Preserve data-while-sharing: An Efficient Technique for Privacy Preserving in OSNs". International Journal on Recent and Innovation Trends in Computing and Communication 11, n. 9 (5 novembre 2023): 3341–53. http://dx.doi.org/10.17762/ijritcc.v11i9.9540.
Srivastava, Agrima, e G. Geethakumari. "Privacy preserving solution to prevent classification inference attacks in online social networks". International Journal of Data Science 4, n. 1 (2019): 31. http://dx.doi.org/10.1504/ijds.2019.098357.
Srivastava, Agrima, e G. Geethakumari. "Privacy preserving solution to prevent classification inference attacks in online social networks". International Journal of Data Science 4, n. 1 (2019): 31. http://dx.doi.org/10.1504/ijds.2019.10019813.
Lakshmanan, Nitya, Abdelhak Bentaleb, Byoungjun Choi, Roger Zimmermann, Jun Han e Min Suk Kang. "On Privacy Risks of Watching YouTube over Cellular Networks with Carrier Aggregation". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, n. 1 (29 marzo 2022): 1–22. http://dx.doi.org/10.1145/3517261.
He, Xiaoyun, e Haibing Lu. "Detecting and preventing inference attacks in online social networks: A data-driven and holistic framework". Journal of Information Privacy and Security 13, n. 3 (3 luglio 2017): 104–19. http://dx.doi.org/10.1080/15536548.2017.1357383.
Ruan, Yefeng, e Arjan Durresi. "A survey of trust management systems for online social communities – Trust modeling, trust inference and attacks". Knowledge-Based Systems 106 (agosto 2016): 150–63. http://dx.doi.org/10.1016/j.knosys.2016.05.042.
R, Balamurugan, Dhivakar M, Muruganantham G e Ramprakash S. "Securing Heterogeneous Privacy Protection in Social Network Records based Encryption Scheme". International Journal on Recent and Innovation Trends in Computing and Communication 7, n. 3 (20 marzo 2019): 10–13. http://dx.doi.org/10.17762/ijritcc.v7i3.5249.
Basati, Amir, Josep M. Guerrero, Juan C. Vasquez, Najmeh Bazmohammadi e Saeed Golestan. "A Data-Driven Framework for FDI Attack Detection and Mitigation in DC Microgrids". Energies 15, n. 22 (15 novembre 2022): 8539. http://dx.doi.org/10.3390/en15228539.
Tesi sul tema "Online Inference Attacks":
Alipour, Pijani Bizhan. "Attaques par inférence d'attributs sur les publications des réseaux sociaux". Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0009.
Online Social Networks (OSN) are full of personal information such as gender, age, relationship status. The popularity and growth of OSN have rendered their platforms vulnerable to malicious activities and increased user privacy concerns. The privacy settings available in OSN do not prevent users from attribute inference attacks where an attacker seeks to illegitimately obtain their personal attributes (such as the gender attribute) from publicly available information. Disclosure of personal information can have serious outcomes such as personal spam, bullying, profile cloning for malicious activities, or sexual harassment. Existing inference techniques are either based on the target user behavior analysis through their liked pages and group memberships or based on the target user friend list. However, in real cases, the amount of available information to an attacker is small since users have realized the vulnerability of standard attribute inference attacks and concealed their generated information. To increase awareness of OSN users about threats to their privacy, in this thesis, we introduce a new class of attribute inference attacks against OSN users. We show the feasibility of these attacks from a very limited amount of data. They are applicable even when users hide all their profile information and their own comments. Our proposed methodology is to analyze Facebook picture metadata, namely (i) alt-text generated by Facebook to describe picture contents, and (ii) commenters’ words and emojis preferences while commenting underneath the picture, to infer sensitive attributes of the picture owner. We show how to launch these inference attacks on any Facebook user by i) handling online newly discovered vocabulary using a retrofitting process to enrich a core vocabulary that was built during offline training and ii) computing several embeddings for textual units (e.g., word, emoji), each one depending on a specific attribute value. Finally, we introduce ProPic, a protection mechanism that selects comments to be hidden in a computationally efficient way while minimizing utility loss according to a semantic measure. The proposed mechanism can help end-users to check their vulnerability to inference attacks and suggests comments to be hidden in order to mitigate the attacks. We have determined the success of the attacks and the protection mechanism by experiments on real data
Chen, Jiayi. "Defending against inference attack in online social networks". Thesis, 2017. https://dspace.library.uvic.ca//handle/1828/8364.
Graduate
Capitoli di libri sul tema "Online Inference Attacks":
Abid, Younes, Abdessamad Imine e Michaël Rusinowitch. "Online Testing of User Profile Resilience Against Inference Attacks in Social Networks". In Communications in Computer and Information Science, 105–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00063-9_12.
Eidizadehakhcheloo, Sanaz, Bizhan Alipour Pijani, Abdessamad Imine e Michaël Rusinowitch. "Divide-and-Learn: A Random Indexing Approach to Attribute Inference Attacks in Online Social Networks". In Data and Applications Security and Privacy XXXV, 338–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81242-3_20.
Reza, Khondker Jahid, Md Zahidul Islam e Vladimir Estivill-Castro. "Protection of User-Defined Sensitive Attributes on Online Social Networks Against Attribute Inference Attack via Adversarial Data Mining". In Communications in Computer and Information Science, 230–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49443-8_11.
Alnasser, Walaa, Ghazaleh Beigi e Huan Liu. "An Overview on Protecting User Private-Attribute Information on Social Networks". In Handbook of Research on Cyber Crime and Information Privacy, 102–17. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5728-0.ch006.
Hai-Jew, Shalin. "In Plaintext". In The Dark Web, 255–89. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3163-0.ch012.
Hai-Jew, Shalin. "In Plaintext". In Remote Workforce Training, 231–64. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5137-1.ch011.
Arul, E., A. Punidha, K. Gunasekaran, P. Radhakrishnan e VD Ashok Kumar. "Malicious Attack Identification Using Deep Non Linear Bag-of-Words (FAI-DLB)". In Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210110.
Atti di convegni sul tema "Online Inference Attacks":
Liu, Junlin, e Xinchen Lyu. "Distance-Based Online Label Inference Attacks Against Split Learning". In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096955.
Kido, Hiroyuki, e Keishi Okamoto. "A Bayesian Approach to Argument-Based Reasoning for Attack Estimation". In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/36.
Kumar, Ashish, e N. C. Rathore. "Improving Attribute Inference Attack Using Link Prediction in Online Social Networks". In International Conference on Recent Advances in Mathematics, Statistics and Computer Science 2015. WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789814704830_0046.