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Journal articles on the topic 'Privacy preserving in an online social network'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Dai, Chenyun, Fang-Yu Rao, Traian Marius Truta, and Elisa Bertino. "Privacy-Preserving Assessment of Social Network Data Trustworthiness." International Journal of Cooperative Information Systems 23, no. 02 (2014): 1441004. http://dx.doi.org/10.1142/s0218843014410044.

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Extracting useful knowledge from social network datasets is a challenging problem. While large online social networks such as Facebook and LinkedIn are well known and gather millions of users, small social networks are today becoming increasingly common. Many corporations already use existing social networks to connect to their customers. Seeing the increasing usage of small social networks, such companies will likely start to create in-house online social networks where they will own the data shared by customers. The trustworthiness of these online social networks is essentially important for

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2

Li, Na, Nan Zhang, and Sajal Das. "Preserving Relation Privacy in Online Social Network Data." IEEE Internet Computing 15, no. 3 (2011): 35–42. http://dx.doi.org/10.1109/mic.2011.26.

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Mahalakshmi, M., and C. Kotteeswaran. "Automatic Privacy Preserving Recommendation for Online Social Network." Journal of Computational and Theoretical Nanoscience 15, no. 6 (2018): 2009–13. http://dx.doi.org/10.1166/jctn.2018.7398.

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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 (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 di

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5

Tang, Chunming, and Cailing Cai. "Verifiable mobile online social network privacy-preserving location sharing scheme." Concurrency and Computation: Practice and Experience 29, no. 24 (2017): e4238. http://dx.doi.org/10.1002/cpe.4238.

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6

J, Sharath Kumar, and Maheswari N. "A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 112. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19587.

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In this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone’s life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone’s personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to

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Kekulluoglu, Dilara, Nadin Kokciyan, and Pinar Yolum. "Preserving Privacy as Social Responsibility in Online Social Networks." ACM Transactions on Internet Technology 18, no. 4 (2018): 1–22. http://dx.doi.org/10.1145/3158373.

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8

Aghasian, Erfan, Saurabh Garg, and James Montgomery. "A Privacy-Enhanced Friending Approach for Users on Multiple Online Social Networks." Computers 7, no. 3 (2018): 42. http://dx.doi.org/10.3390/computers7030042.

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Online social network users share their information in different social sites to establish connections with individuals with whom they want to be a friend. While users share all their information to connect to other individuals, they need to hide the information that can bring about privacy risks for them. As user participation in social networking sites rises, the possibility of sharing information with unknown users increases, and the probability of privacy breaches for the user mounts. This work addresses the challenges of sharing information in a safe manner with unknown individuals. Curre

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9

Bahri, Leila, Barbara Carminati, and Elena Ferrari. "Decentralized privacy preserving services for Online Social Networks." Online Social Networks and Media 6 (June 2018): 18–25. http://dx.doi.org/10.1016/j.osnem.2018.02.001.

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10

Jing, Tao, Qiancheng Chen, and Yingkun Wen. "A Probabilistic Privacy Preserving Strategy for Word-of-Mouth Social Networks." Wireless Communications and Mobile Computing 2018 (July 8, 2018): 1–12. http://dx.doi.org/10.1155/2018/6031715.

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An online social network (OSN) is a platform that makes people communicate with friends, share messages, accelerate business, and enhance teamwork. In the OSN, privacy issues are increasingly concerned, especially in private message leaks in word-of-mouth. A user’s privacy may be leaked out by acquaintances without user’s consent. In this paper, an integrated system is designed to prevent this illegal privacy leak. In particular, we only use the method of space vector model to determine whether the user’s private message is really leaked. Canary traps techniques are used to detect leakers. The

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11

Kumaran, Umapathy, and Khare Neelu. "An Efficient & Secure Content Contribution and Retrieval Content in Online Social Networks Using Level-level Security Optimization & Content Visualization Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (2018): 807–16. https://doi.org/10.11591/ijeecs.v10.i2.pp807-816.

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Online Social Networks (OSNs) is currently popular interactive media to establish the communication, share and disseminate a considerable amount of human life data. Daily and continuous communications imply the exchange of several types of content, including free text, image, audio, and video data. Security is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). However, there are no contentbased preferences supported, and therefore it is not possible to prevent undesired messages. Providing the service is not only a matter of using previous

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12

Yang, Guangcan, Shoushan Luo, Yang Xin, et al. "A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks." Applied Sciences 10, no. 23 (2020): 8402. http://dx.doi.org/10.3390/app10238402.

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With the advent of intelligent handheld devices, location sharing becomes one of the most popular services in mobile online social networks (mOSNs). In location-sharing services, users can enjoy a better social experience by updating their real-time location information. However, the leakage of private information may hinder the further development of location-sharing services. Although many solutions have been proposed to protect users’ privacy, the privacy-utility trade-offs must be considered. Therefore, we propose a new scheme called search efficient privacy-preserving location-sharing (SE

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13

Parimala, M. "Safeguarding User Information in Contextual Social Network." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50819.

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This work discusses the emerging data protection issues in context-aware social networks and suggests a model for protecting user privacy through robust security, context-aware data handling, and clear policies. We elaborate on the use of encryption, differential privacy, and anonymization methods to reduce data exposure while facilitating system tailored interactions. We also emphasize the necessity of secure consent management and the need for user autonomy over shared data. With the integration of these features, context-aware social networks are now capable of achieving privacy and persona

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14

Majeed, Abdul, Safiullah Khan, and Seong Oun Hwang. "A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks." Electronics 11, no. 13 (2022): 1931. http://dx.doi.org/10.3390/electronics11131931.

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Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other hand, this ever-growing use of OSNs enables adversaries to launch various privacy attacks to compromise users’ accounts as well as to steal other sensitive information via statistical matching. In general, a privacy attack is carried out by the exercise of linking personal data available on the OSN site and social grap

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15

Chiou, Shin-Yan, and Chi-Shiu Luo. "An Authenticated Privacy-Preserving Mobile Matchmaking Protocol Based on Social Connections with Friendship Ownership." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/637985.

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The increase of mobile device use for social interaction drives the proliferation of online social applications. However, it prompts a series of security and existence problems. Some common problems are the authenticity of social contacts, the privacy of online communication, and the lack of physical interaction. This work presents mobile private matchmaking protocols that allow users to privately and immediately search the targets which match their planning purposes via mobile devices and wireless network. Based on social networks, the relationships of targets can be unlimited or limited to f

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16

TwinkleMathew, Amal, S. Saravana Kumar, and Karthikeyan M. "User Intended Privacy Preserving Models in Online Social Networks." International Journal of Computer Applications 113, no. 15 (2015): 28–32. http://dx.doi.org/10.5120/19903-2014.

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17

Wang, Zhe, and Naftaly H. Minsky. "A Novel, Privacy Preserving, Architecture for Online Social Networks." EAI Endorsed Transactions on Collaborative Computing 1, no. 5 (2015): 150806. http://dx.doi.org/10.4108/eai.17-12-2015.150806.

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18

Kumar, Saurabh, and Pradeep Kumar. "Upper approximation based privacy preserving in online social networks." Expert Systems with Applications 88 (December 2017): 276–89. http://dx.doi.org/10.1016/j.eswa.2017.07.010.

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19

Cutillo, Leucio, Refik Molva, and Thorsten Strufe. "Safebook: A privacy-preserving online social network leveraging on real-life trust." IEEE Communications Magazine 47, no. 12 (2009): 94–101. http://dx.doi.org/10.1109/mcom.2009.5350374.

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20

Chen, Jiayi, Jianping He, Lin Cai, and Jianping Pan. "Disclose More and Risk Less: Privacy Preserving Online Social Network Data Sharing." IEEE Transactions on Dependable and Secure Computing 17, no. 6 (2020): 1173–87. http://dx.doi.org/10.1109/tdsc.2018.2861403.

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21

M, Shamila, G. Rekha, and K. Vinuthna Reddy. "Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs." International Journal of Experimental Research and Review 41, Spl Vol (2024): 180–94. http://dx.doi.org/10.52756/ijerr.2024.v41spl.015.

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Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation o

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22

Chaudhary, Pooja, B. B. Gupta, and Shashank Gupta. "A Framework for Preserving the Privacy of Online Users Against XSS Worms on Online Social Network." International Journal of Information Technology and Web Engineering 14, no. 1 (2019): 85–111. http://dx.doi.org/10.4018/ijitwe.2019010105.

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This article presents a hybrid framework i.e. OXSSD (Online Social Network-Based XSS-Defender) that explores cross-site scripting (XSS) attack vectors at the vulnerable points in web applications of social networks. Initially, during training phase, it generates the views for each request and formulates the access control list (ACL) which encompasses all the privileges a view can have. It also ascertains all possible injection points for extracting malicious attack vectors. Secondly, during recognition phase, after action authentication XSS attack vectors are retrieved from the extracted injec

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23

Nilesh, Kulal*. "SURVEY ON FRIEND RECOMMENDATION METHODS FOR ONLINE SOCIAL NETWORKS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 1 (2018): 207–12. https://doi.org/10.5281/zenodo.1135979.

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Online social networking sites, like Facebook and Google+, provides a new communication service for online users to stay in contact. With this service, user can share any kind of information with their friends and family over internet. Such communication among user generates the huge amount of data on social networking sites. But with this facility, question is arises regarding mining useful knowledge from such huge amount of data. The analysis of such data will be used in various applications for identification of potential users and promotion of items according to the user interest. From ano

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24

CHEN, Juan, Shen SU, and Xianzhi WANG. "Towards Privacy-Preserving Location Sharing over Mobile Online Social Networks." IEICE Transactions on Information and Systems E102.D, no. 1 (2019): 133–46. http://dx.doi.org/10.1587/transinf.2018edp7187.

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25

Sun, Weiwei, Jiantao Zhou, Shuyuan Zhu, and Yuan Yan Tang. "Robust Privacy-Preserving Image Sharing over Online Social Networks (OSNs)." ACM Transactions on Multimedia Computing, Communications, and Applications 14, no. 1 (2018): 1–22. http://dx.doi.org/10.1145/3165265.

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26

Zhang, Shiwen, Xiong Li, Haowen Liu, Yaping Lin, and Arun Kumar Sangaiah. "A privacy-preserving friend recommendation scheme in online social networks." Sustainable Cities and Society 38 (April 2018): 275–85. http://dx.doi.org/10.1016/j.scs.2017.12.031.

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27

Xu, Lei, Ting Bao, Liehuang Zhu, and Yan Zhang. "Trust-Based Privacy-Preserving Photo Sharing in Online Social Networks." IEEE Transactions on Multimedia 21, no. 3 (2019): 591–602. http://dx.doi.org/10.1109/tmm.2018.2887019.

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28

Bhattacharya, Munmun, Sandip Roy, Kamlesh Mistry, Hubert P. H. Shum, and Samiran Chattopadhyay. "A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications." IEEE Access 8 (2020): 221330–51. http://dx.doi.org/10.1109/access.2020.3043621.

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29

Wanda, Putra. "Modern Privacy-Preserving and Security Schemes in Social Networks: A Review." International Journal of Informatics and Computation 3, no. 2 (2022): 23. http://dx.doi.org/10.35842/ijicom.v3i2.39.

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Online Social Network (OSN) is a popular application to exchange messages over the internet. However, millions of users are still under threat because of protection drawbacks. Many papers have proposed security methods, including firewalls, protocols, cryptography, statistical analysis, even learning algorithms. This paper provides an overview of privacy and security issues and describes multiple OSN protection techniques. We present various security schemes in OSNs and outline existing solutions to mitigate those attacks. This paper also discusses future research directions regarding OSN secu

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Rohit, M., and Ceronmani Sharmila. "A Secure User Image Privacy Preserving Technique to Avoid Clone Attack in Online Social Network." Journal of Computational and Theoretical Nanoscience 17, no. 5 (2020): 2304–7. http://dx.doi.org/10.1166/jctn.2020.8888.

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The hundreds of thousands of active users everywhere in the globe use online social community, inclusive of Facebook, Twitter, Tumbler and LinkedIn. This makes it handy for fake profile cloning and compromise user information. This system, uses information to be hidden in a profile photos with a hidden information to detect profiles which are fake and any attacks that’s taken place by botnet. This project presents the detection mechanisms of social network based attacks that takes place online, analysis of the profile with interval in time and sequenced Protocol. This project we have proposed

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31

Yi, Yuzi, Nafei Zhu, Jingsha He, Anca Delia Jurcut, Xiangjun Ma, and Yehong Luo. "A privacy-dependent condition-based privacy-preserving information sharing scheme in online social networks." Computer Communications 200 (February 2023): 149–60. http://dx.doi.org/10.1016/j.comcom.2023.01.010.

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32

Cai, Ying, Shunan Zhang, Hongke Xia, Yanfang Fan, and Haochen Zhang. "A Privacy-Preserving Scheme for Interactive Messaging Over Online Social Networks." IEEE Internet of Things Journal 7, no. 8 (2020): 6817–27. http://dx.doi.org/10.1109/jiot.2020.2986341.

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33

Guo, Linke, Chi Zhang, Yuguang Fang, and Phone Lin. "A Privacy-Preserving Attribute-Based Reputation System in Online Social Networks." Journal of Computer Science and Technology 30, no. 3 (2015): 578–97. http://dx.doi.org/10.1007/s11390-015-1547-9.

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34

Shamila, Et al. "A Hybrid Probabilistic Privacy Preserving Based Community Detection Model on Online Social Networking Data." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 626–35. http://dx.doi.org/10.17762/ijritcc.v11i9.8852.

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Privacy preserving plays a vital role on the online social networking sites due to high dimensionality and data size. Community detection is used to find the social relationships among the node edges and links. However, most of the conventional models are difficult to process the community structure detection due to high computational time and memory. Also, these models require contextual weighted nodes information for privacy preserving process. In order to overcome these issues, an advanced probabilistic weighted based community detection and privacy preserving framework is developed on the

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35

Nallamolu, Sathish, and Srinivas Padmanabhuni. "A Privacy Preserving Generative Adversarial Network for Image Data." ITM Web of Conferences 53 (2023): 03004. http://dx.doi.org/10.1051/itmconf/20235303004.

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The extensive usage of online applications and social media has raised serious concerns from the public regarding the exposure of their personal information. So, there is a strong need for data anonymization to prevent privacy breaches and leakages. The era of attacks on databases and servers is an old trend. Now, most attacks are based on earning access to users’ private data. There are techniques like k-anonymity and l-diversity to protect Personally Identifiable Information (PII) from adversaries. However, these techniques still cannot provide security from homogeneity attacks, and their ap

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36

Rajput, Dr Ubaidullah, Ahsan Ansari, Sammer Zai, and Aakash Narwani. "Privacy Preserving Location based Services Through K-Anonymized Vehicular Social Network." Quaid-e-Awam University Research Journal of Engineering, Science & Technology 18, no. 02 (2020): 163–68. http://dx.doi.org/10.52584/qrj.1802.24.

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Location-based services (LBS) are the services that are used through users’ mobile devices and provide them the information regarding nearby restaurants, hospitals, gas stations, shopping malls, cinema (to name a few). While using LBS, a user needs to provide his/her location coordinates (geo-coordinates) to the LBS server. The revelation of a user’s location may seriously jeopardize his/her privacy. A common solution to this problem is the use of an intermediate anonymizer server that obfuscate the real location of a user among k other users. However, in this scenario, the anonymizer server m

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37

Ulusoy, Onuralp, and Pinar Yolum. "PANOLA: A Personal Assistant for Supporting Users in Preserving Privacy." ACM Transactions on Internet Technology 22, no. 1 (2022): 1–32. http://dx.doi.org/10.1145/3471187.

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Privacy is the right of individuals to keep personal information to themselves. When individuals use online systems, they should be given the right to decide what information they would like to share and what to keep private. When a piece of information pertains only to a single individual, preserving privacy is possible by providing the right access options to the user. However, when a piece of information pertains to multiple individuals, such as a picture of a group of friends or a collaboratively edited document, deciding how to share this information and with whom is challenging. The prob

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38

WANG, SHYUE-LIANG, YU-CHUAN TSAI, HUNG-YU KAO, I.-HSIEN TING, and TZUNG-PEI HONG. "SHORTEST PATHS ANONYMIZATION ON WEIGHTED GRAPHS." International Journal of Software Engineering and Knowledge Engineering 23, no. 01 (2013): 65–79. http://dx.doi.org/10.1142/s0218194013400056.

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Due to the proliferation of online social networking, a large number of personal data are publicly available. As such, personal attacks, reputational, financial, or family losses might occur once this personal and sensitive information falls into the hands of malicious hackers. Research on Privacy-Preserving Network Publishing has attracted much attention in recent years. But most work focus on node de-identification and link protection. In academic social networks, business transaction networks, and transportation networks, etc, node identities and link structures are public knowledge but wei

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39

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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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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41

Guo, Linke, Chi Zhang, and Yuguang Fang. "A Trust-Based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks." IEEE Transactions on Dependable and Secure Computing 12, no. 4 (2015): 413–27. http://dx.doi.org/10.1109/tdsc.2014.2355824.

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42

Tabassum, S. Nasira, and Gangadhara Rao Kancherla. "Optimized Multi-Level Security for Content Contribution and Retrieval in Online Social Networks using a Content Visualization Mechanism." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18395–400. https://doi.org/10.48084/etasr.8968.

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Online social networks have become an integral part of modern communication, providing platforms for users to share personal information, media, and opinions. However, these platforms face significant challenges in preserving user privacy while ensuring efficient data retrieval and maintaining data integrity. Existing privacy preservation methods, such as PPK-MEANS, CFCAF, and CLDPP, are limited in their ability to handle the growing complexity and scale of user data, often leading to inefficiencies such as high Content Retrieval Time (CRT), increased Information Loss (IL), and compromised dat

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Putra, Wanda, Endah Hiswati Marselina, and J. Jie Huang. "DeepOSN: Bringing deep learning as malicious detection scheme in online social network." International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (2020): 146–54. https://doi.org/10.11591/ijai.v9.i1.pp146-154.

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Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Currently, many research communities have proposed learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Deep learning is a growing algorithm that gains a big success in computer vision problems. In this paper, we propose a novel deep learning architecture to establish the OSN security technique to become more intelligent for dete

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Halawi, Ola N., Faisal N. Abu-Khzam, and Sergio Thoumi. "A Multi-Objective Degree-Based Network Anonymization Method." Algorithms 16, no. 9 (2023): 436. http://dx.doi.org/10.3390/a16090436.

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Enormous amounts of data collected from social networks or other online platforms are being published for the sake of statistics, marketing, and research, among other objectives. The consequent privacy and data security concerns have motivated the work on degree-based data anonymization. In this paper, we propose and study a new multi-objective anonymization approach that generalizes the known degree anonymization problem and attempts at improving it as a more realistic model for data security/privacy. Our suggested model guarantees a convenient privacy level, based on modifying the degrees in

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45

Liu, Zheli, Dejiang Luo, Jin Li, Xiaofeng Chen, and Chunfu Jia. "N-Mobishare: new privacy-preserving location-sharing system for mobile online social networks." International Journal of Computer Mathematics 93, no. 2 (2014): 384–400. http://dx.doi.org/10.1080/00207160.2014.917179.

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46

Xiao, Xi, Chunhui Chen, Arun Kumar Sangaiah, Guangwu Hu, Runguo Ye, and Yong Jiang. "CenLocShare: A centralized privacy-preserving location-sharing system for mobile online social networks." Future Generation Computer Systems 86 (September 2018): 863–72. http://dx.doi.org/10.1016/j.future.2017.01.035.

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47

Kalyan, Aravind. "Efficient and Secure Mechanism for Privacy Preserving and Data Sharing in Online Social Networks." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (2019): 1–6. http://dx.doi.org/10.22214/ijraset.2019.5001.

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48

Guo, Guanglai, Yan Zhu, Ruyun Yu, William Cheng-Chung Chu, and Di Ma. "A Privacy-Preserving Framework With Self-Governance and Permission Delegation in Online Social Networks." IEEE Access 8 (2020): 157116–29. http://dx.doi.org/10.1109/access.2020.3016041.

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49

Kaosar, Mohammed, and Quazi Mamun. "Privacy-Preserving Interest Group Formation in Online Social Networks (OSNs) Using Fully Homomorphic Encryption." Journal of Information Privacy and Security 10, no. 1 (2014): 44–52. http://dx.doi.org/10.1080/15536548.2014.912909.

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50

Sivasankari K and Uma Maheswari K M. "Privacy Preserving Using K Member Gaussian Kernel Fuzzy C Means and Self Adaptive Honey Badger for Online Social Networks." Journal of Advanced Research in Applied Sciences and Engineering Technology 45, no. 2 (2024): 25–37. http://dx.doi.org/10.37934/araset.45.2.2537.

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An online social network (OSN) gives users a strong platform to communicate and exchange information. Protecting publicly published data from individual identification is the primary problem in sharing social network databases. The most popular method for protecting privacy is anonymizing data, which involves deleting or altering some information while maintaining as much of the original data as feasible. This work presents a combination anonymizing algorithm, which is based on k member Gaussian kernel fuzzy c means clustering and self-adaptive honey badger optimization technique (KGKFCM-SAHBO

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