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Готові списки джерел за темами / Online Inference Attacks

Добірка наукової літератури з теми "Online Inference Attacks"

Автор: Grafiati

Опубліковано: 8 червня 2024

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Статті в журналах з теми "Online Inference Attacks":

1

Gong, Neil Zhenqiang, and Bin Liu. "Attribute Inference Attacks in Online Social Networks." ACM Transactions on Privacy and Security 21, no. 1 (January 6, 2018): 1–30. http://dx.doi.org/10.1145/3154793.

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2

Lachat, Paul, Nadia Bennani, Veronika Rehn-Sonigo, Lionel Brunie, and Harald Kosch. "Detecting Inference Attacks Involving Raw Sensor Data: A Case Study." Sensors 22, no. 21 (October 24, 2022): 8140. http://dx.doi.org/10.3390/s22218140.

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With the advent of sensors, more and more services are developed in order to provide customers with insights about their health and their appliances’ energy consumption at home. To do so, these services use new mining algorithms that create new inference channels. However, the collected sensor data can be diverted to infer personal data that customers do not consent to share. This indirect access to data that are not collected corresponds to inference attacks involving raw sensor data (IASD). Towards these new kinds of attacks, existing inference detection systems do not suit the representation requirements of these inference channels and of user knowledge. In this paper, we propose RICE-M (Raw sensor data based Inference ChannEl Model) that meets these inference channel representations. Based on RICE-M, we proposed RICE-Sy an extensible system able to detect IASDs, and evaluated its performance taking as a case study the MHEALTH dataset. As expected, detecting IASD is proven to be quadratic due to huge sensor data managed and a quickly growing amount of user knowledge. To overcome this drawback, we propose first a set of conceptual optimizations that reduces the detection complexity. Although becoming linear, as online detection time remains greater than a fixed acceptable query response limit, we propose two approaches to estimate the potential of RICE-Sy. The first one is based on partitioning strategies which aim at partitioning the knowledge of users. We observe that by considering the quantity of knowledge gained by a user as a partitioning criterion, the median detection time of RICE-Sy is reduced by 63%. The second approach is H-RICE-SY, a hybrid detection architecture built on RICE-Sy which limits the detection at query-time to users that have a high probability to be malicious. We show the limits of processing all malicious users at query-time, without impacting the query answer time. We observe that for a ratio of 30% users considered as malicious, the median online detection time stays under the acceptable time of 80 , for up to a total volume of 1.2 million user knowledge entities. Based on the observed growth rates, we have estimated that for 5% of user knowledge issued by malicious users, a maximum volume of approximately 8.6 million user’s information can be processed online in an acceptable time.

3

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, no. 9 (November 5, 2023): 3341–53. http://dx.doi.org/10.17762/ijritcc.v11i9.9540.

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Online Social Networks (OSNs) have become one of the major platforms for social interactions, such as building up relationships, sharing personal experiences, and providing other services. Rapid growth in Social Network has attracted various groups like the scientific community and business enterprise to use these huge social network data to serve their various purposes. The process of disseminating extensive datasets from online social networks for the purpose of conducting diverse trend analyses gives rise to apprehensions regarding privacy, owing to the disclosure of personal information disclosed on these platforms. Privacy control features have been implemented in widely used online social networks (OSNs) to empower users in regulating access to their personal information. Even if Online Social Network owners allow their users to set customizable privacy, attackers can still find out users’ private information by finding the relationships between public and private information with some background knowledge and this is termed as inference attack. In order to defend against these inference attacks this research work could completely anonymize the user identity. This research work designs an optimization algorithm that aims to strike a balance between self-disclosure utility and their privacy. This research work proposes two privacy preserving algorithms to defend against an inference attack. The research work design an Privacy-Preserving Algorithm (PPA) algorithm which helps to achieve high utility by allowing users to share their data with utmost privacy. Another algorithm-Multi-dimensional Knapsack based Relation Disclosure Algorithm (mdKP-RDA) that deals with social relation disclosure problems with low computational complexity. The proposed work is evaluated to test the effectiveness on datasets taken from actual social networks. According on the experimental results, the proposed methods outperform the current methods.

4

Srivastava, Agrima, and G. Geethakumari. "Privacy preserving solution to prevent classification inference attacks in online social networks." International Journal of Data Science 4, no. 1 (2019): 31. http://dx.doi.org/10.1504/ijds.2019.098357.

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5

Srivastava, Agrima, and G. Geethakumari. "Privacy preserving solution to prevent classification inference attacks in online social networks." International Journal of Data Science 4, no. 1 (2019): 31. http://dx.doi.org/10.1504/ijds.2019.10019813.

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6

Lakshmanan, Nitya, Abdelhak Bentaleb, Byoungjun Choi, Roger Zimmermann, Jun Han, and 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, no. 1 (March 29, 2022): 1–22. http://dx.doi.org/10.1145/3517261.

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One's core values, personality, and social status may be reflected in the watch history of online video streaming services such as YouTube. Unfortunately, several prior research work demonstrates that man-in-the-middle or malware-assisted attackers can accurately infer the titles of encrypted streaming videos by exploiting the inherent correlation between the encoded video contents and the traffic rate changes. In this paper, we present a novel video-inference attack called Moba that further exacerbates the problem by only requiring the adversary to simply eavesdrop the broadcast messages of a primary cell of a targeted user's cellular phone. Our attack utilizes a side channel in modern cellular networks that leaks the number of actively transmitting cells for each user. We show that this seemingly harmless system information leakage can be used to achieve practical video-inference attacks. To design effective video-inference attacks, we augment the coarse-grained side-channel measurements with precise timing information and estimate the traffic bursts of encrypted video contents. The Moba attack considers an adversary-chosen set of suspect YouTube videos, from which a targeted user may watch some videos during the attack. We confirm the feasibility of Moba in identifying the exact YouTube video title (if it is from the suspect set) via our over-the-air experiments conducted in LTE-Advanced networks in two countries. Moba can be effective in verifying whether a targeted user watches any of the suspect videos or not; e.g., precision of 0.98 is achieved after observing six-minutes of a single video play. When further allowed to observe multiple video plays, Moba adversary is able to identify whether the targeted user frequently watches the suspect videos with a probability close to one and a near-zero false positive rate. Finally, we present a simple padding-based countermeasure that significantly reduces the attack effectiveness without sacrificing any cellular radio resources.

7

He, Xiaoyun, and Haibing Lu. "Detecting and preventing inference attacks in online social networks: A data-driven and holistic framework." Journal of Information Privacy and Security 13, no. 3 (July 3, 2017): 104–19. http://dx.doi.org/10.1080/15536548.2017.1357383.

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8

Ruan, Yefeng, and Arjan Durresi. "A survey of trust management systems for online social communities – Trust modeling, trust inference and attacks." Knowledge-Based Systems 106 (August 2016): 150–63. http://dx.doi.org/10.1016/j.knosys.2016.05.042.

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9

R, Balamurugan, Dhivakar M, Muruganantham G, and 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, no. 3 (March 20, 2019): 10–13. http://dx.doi.org/10.17762/ijritcc.v7i3.5249.

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This survey places of interest the major issues concerning privacy and security in online social networks. Firstly, we discuss investigate that aims to protect user data from the an assortment of attack vantage points together with other users, advertisers, third party request developers, and the online social arrangement provider itself. Next we cover social network supposition of user attributes, locate hubs, and link prediction. Because online social networks are so saturated with sensitive information, network inference plays a major privacy role. Social Networking sites go upwards since of all these reasons. In recent years indicates that for many people they are now the mainstream communication knowledge. Social networking sites come under few of the most frequently browsed categories websites in the world. Nevertheless Social Networking sites are also vulnerable to various problems threats and attacks such as revelation of information, identity thefts etc. Privacy practice in social networking sites often appear convoluted as in sequence sharing stands in discord with the need to reduce disclosure-related abuses. Facebook is one such most popular and widely used Social Networking sites which have its own healthy set of Privacy policy.

10

Basati, Amir, Josep M. Guerrero, Juan C. Vasquez, Najmeh Bazmohammadi, and Saeed Golestan. "A Data-Driven Framework for FDI Attack Detection and Mitigation in DC Microgrids." Energies 15, no. 22 (November 15, 2022): 8539. http://dx.doi.org/10.3390/en15228539.

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This paper proposes a Data-Driven (DD) framework for the real-time monitoring, detection, and mitigation of False Data Injection (FDI) attacks in DC Microgrids (DCMGs). A supervised algorithm is adopted in this framework to continuously estimate the output voltage and current for all Distributed Generators (DGs) with acceptable accuracy. Accordingly, among the various evaluated supervised DD algorithms, Adaptive Neuro-Fuzzy Inference Systems (ANFISs) are utilized because of their low computational burden, efficiency in operation, and simplicity in design and implementation in a distributed control system. The proposed framework is based on the residual analysis of the generated error signal between the estimated and actual sensed signals. The proposed framework detects and mitigates the cyber-attack depending on trends in generated error signals. Moreover, by applying Online Change Point Detection (OCPD), the need for a static user-defined threshold for the residual analysis of the generated error signal is dispelled. Finally, the proposed method is validated in a MATLAB/Simulink testbed, considering the resilience, effectiveness, accuracy, and robustness of multiple case study scenarios.

Більше джерел

Дисертації з теми "Online Inference Attacks":

1

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.

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Les réseaux sociaux contiennent de nombreuses informations personnelles telles que le genre, l'âge ou le statut d'une relation. Leur popularité et leur importance en font des cibles privilégiés pour des activités malveillantes menaçant la vie privée des utilisateurs. Les paramètres de sécurité disponibles sur les réseaux sociaux n'empêchent pas les attaques par inférence d'attribut, qui consistent pour l'attaquant à obtenir des données privées (comme le genre) à partir d'informations publiques. La divulgation d'une information personnelle peut avoir des conséquences négatives comme devenir la cible de spams, de harcèlements, ou se faire cloner son profil. Les techniques d'inférence les plus connues s'appuient soit sur l'analyse du comportement de l'utilisateur cible à travers ses préférences (e.g., likes) et ses groupes, soit sur ses listes d'amis. Cependant, en pratique, les informations disponibles pour ces attaques sont souvent limitées car beaucoup d'utilisateurs ont pris conscience des menaces et préfèrent protéger leurs données. Pour que les usagers des réseaux sociaux comprennent mieux les risques encourus par leur vie privée, dans cette thèse nous introduisons une nouvelle classe d'attaques par inférence sur les attributs de ces usagers. Nous montrons que ces attaques nécessitent très peu d'information. Elles s'appliquent même à des usagers qui protègent les éléments de leur profil ainsi que leurs commentaires. La méthode que nous proposons consiste à analyser les métadatas d'une image publiée sur Facebook, à savoir i) les tags engendrés par Facebook pour décrire les images (e.g., pour les usagers malvoyants), et ii) les commentaires sous formes textuelle ou d'émojis déposés sous l'image. Nous montrons comment réaliser ces attaques sur un utilisateur de Facebook en i) appliquant une technique de retrofitting pour traiter le vocabulaire rencontré en ligne et qui ne figurait pas dans la base d'apprentissage et ii) en calculant plusieurs plongements pour les unités textuelles (e.g., mot, emoji) chacun dépendant d'une valeur spécifique d'un attribut. Finalement nous proposons ProPic, un mécanisme de protection qui sélectionne de manière rapide des commentaires à cacher toute en minimisant la perte d'utilité, définie par une mesure sémantique. Le système permet aux utilisateurs de vérifier s'ils sont vulnérables à des attaques par inférence et, le cas échéant de suggérer les commentaires à cacher pour prévenir ces attaques. Nous avons pu vérifier l'efficacité de l'approche par des expérimentations sur des données réelles
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

2

Chen, Jiayi. "Defending against inference attack in online social networks." Thesis, 2017. https://dspace.library.uvic.ca//handle/1828/8364.

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The privacy issues in online social networks (OSNs) have been increasingly arousing the public awareness since it is possible for attackers to launch several kinds of attacks to obtain users' sensitive and private information by exploiting the massive data obtained from the networks. Even if users conceal their sensitive information, attackers can infer their secrets by studying the correlations between private and public information with background knowledge. To address these issues, the thesis focuses on the inference attack and its countermeasures. First, we study how to launch the inference attack to profile OSN users via relationships and network characteristics. Due to both user privacy concerns and unformatted textual information, it is quite difficult to build a completely labeled social network directly. However, both social relations and network characteristics can help attribute inference to profile OSN users. We propose several attribute inference models based on these two factors and implement them with Naive Bayes, Decision Tree, and Logistic Regression. Also, to study network characteristics and evaluate the performance of our proposed models, we use a well-labeled Google employee social network extracted from Google+ for inferring the social roles of Google employees. The experiment results demonstrate that the proposed models are effective in social role inference with Dyadic Label Model performing the best. Second, we model the general inference attack and formulate the privacy-preserving data sharing problem to defend against the attack. The optimization problem is to maximize the users' self-disclosure utility while preserving their privacy. We propose two privacy-preserving social network data sharing methods to counter the inference attack. One is the efficient privacy-preserving disclosure algorithm (EPPD) targeting the high utility, and the other is to convert the original problem into a multi-dimensional knapsack problem (d-KP) which can be solved with a low computational complexity. We use real-world social network datasets to evaluate the performance. From the results, the proposed methods achieve a better performance when compared with the existing ones. Finally, we design a privacy protection authorization framework based on the OAuth 2.0 protocol. Many third-party services and applications have integrated the login services of popular social networking sites, such as Facebook and Google+, and acquired user information to enrich their services by requesting user's permission. However, due to the inference attack, it is still possible to infer users' secrets. Therefore, we embed our privacy-preserving data sharing algorithms in the implementation of OAuth 2.0 framework and propose RANPriv-OAuth2 to protect users' privacy from the inference attack.
Graduate

Частини книг з теми "Online Inference Attacks":

1

Abid, Younes, Abdessamad Imine, and 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.

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2

Eidizadehakhcheloo, Sanaz, Bizhan Alipour Pijani, Abdessamad Imine, and 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.

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3

Reza, Khondker Jahid, Md Zahidul Islam, and 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.

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4

Alnasser, Walaa, Ghazaleh Beigi, and 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.

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Анотація:

Online social networks enable users to participate in different activities, such as connecting with each other and sharing different contents online. These activities lead to the generation of vast amounts of user data online. Publishing user-generated data causes the problem of user privacy as this data includes information about users' private and sensitive attributes. This privacy issue mandates social media data publishers to protect users' privacy by anonymizing user-generated social media data. Existing private-attribute inference attacks can be classified into two classes: friend-based private-attribute attacks and behavior-based private-attribute attacks. Consequently, various privacy protection models are proposed to protect users against private-attribute inference attacks such as k-anonymity and differential privacy. This chapter will overview and compare recent state-of-the-art researches in terms of private-attribute inference attacks and corresponding anonymization techniques. In addition, open problems and future research directions will be discussed.

5

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.

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People have long gone online to groom their online identities, to communicate some aspects of themselves in the real. The information shared is purposive and strategic. Inevitably, the information is selective and incomplete. The cyber may evoke something about the physical only to a degree, in the cyber-physical confluence. In an asymmetrical information environment, those who have the most accurate and requisite information often have the advantage. It is said that much of intelligence is conducted using Open-Source Intelligence (OSINT), which suggests a need for reading between the lines of publicly released information; indeed, much of life is conducted in online public spaces. A number of tools enable the extraction and analysis of information from public sites. When used in combination, these tools may create a fairly clear sense of the online presences of various individuals or organizations or networks online for increased transparency. This chapter describes some of the tools (Maltego Radium™ and Network Overview, Discovery, and Exploration for Excel/NodeXL™) that may be used to increase the knowability of others in the creation of various profiles. This includes some light applications of “inference attacks” based on publicly available information. Further information may be captured from the Hidden Web through tools designed to crawl that understructure, and this potential is addressed a little as well.

6

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.

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Анотація:

People have long gone online to groom their online identities, to communicate some aspects of themselves in the real. The information shared is purposive and strategic. Inevitably, the information is selective and incomplete. The cyber may evoke something about the physical only to a degree, in the cyber-physical confluence. In an asymmetrical information environment, those who have the most accurate and requisite information often have the advantage. It is said that much of intelligence is conducted using Open-Source Intelligence (OSINT), which suggests a need for reading between the lines of publicly released information; indeed, much of life is conducted in online public spaces. A number of tools enable the extraction and analysis of information from public sites. When used in combination, these tools may create a fairly clear sense of the online presences of various individuals or organizations or networks online for increased transparency. This chapter describes some of the tools (Maltego Radium™ and Network Overview, Discovery, and Exploration for Excel/NodeXL™) that may be used to increase the knowability of others in the creation of various profiles. This includes some light applications of “inference attacks” based on publicly available information. Further information may be captured from the Hidden Web through tools designed to crawl that understructure, and this potential is addressed a little as well.

7

Arul, E., A. Punidha, K. Gunasekaran, P. Radhakrishnan, and 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.

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Online media have flourished in modern years to connect with the world. Most of those stuff users share on blogs like facebook, twitter and many other are pessimistic or just middle spirited. Further, an increasingly professional anti - spyware technologies are dependent on Machine Learning(ML) technology to secure malicious consumers. Over the past few years, revolutionary learning approaches have yielded remarkable outcomes and have immediately generated photos, characters and text interpretations of dynamic weak points. The Purple consumer frequency makes the troll and attacker aim an enticing one. The users will learn the controversial topics and techniques used by malware from articles with ties to harmful material and bogus applications. It is essential to build and customize a lot of potential functionality in vulnerability and application developers around the world. To represent a public web firmware assault with deep logistic inference using Extreme Spontaneous Tree (FAI-DLB). A corresponding output device is named harmful or benign by training an FAI-DLB with different modulation clustered with such a normal or anomalous API. It was therefore equipped to locate a suspicious sequence in unidentified firmware of FAI Deep LB. The outcome demonstrates a good actual meaning of 96.25% and a low spyware assault of 0.03%.

Тези доповідей конференцій з теми "Online Inference Attacks":

1

Liu, Junlin, and 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.

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2

Kido, Hiroyuki, and 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.

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The web is a source of a large amount of arguments and their acceptability statuses (e.g., votes for and against the arguments). However, relations existing between the fore-mentioned arguments are typically not available. This study investigates the utilisation of acceptability semantics to statistically estimate an attack relation between arguments wherein the acceptability statuses of arguments are provided. A Bayesian network model of argument-based reasoning is defined in which Dung's theory of abstract argumentation gives the substance of Bayesian inference. The model correctness is demonstrated by analysing properties of estimated attack relations and illustrating its applicability to online forums.

3

Kumar, Ashish, and 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.

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