In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution.

在当今时代，在线社交网络已经成为人类社会的重要现象。但是，很大一部分用户并不知道其中涉及的安全性和隐私问题。人们倾向于发布敏感的私人信息，例如出生日期，手机号码，签到地点，居住地点，情感，配偶和其他家庭成员的姓名等，这可能会造成灾难性的后果。通过监视他们的社交网络更新，网络攻击者首先收集用户的公共信息，该信息将进一步用于获取他们的机密信息（如银行详细信息等）并发起安全攻击，例如假冒身份攻击。这种攻击或信息泄漏可能会严重影响他们的生活。在这个充满科技的时代 用户必须非常了解在线社交网络所涉及的潜在风险，这势在必行。本文全面调查了在线社交网络的发展，其相关的风险和解决方案。讨论了各种安全模型和最新算法，以及使用机器学习，深度学习和统计测试的比较元分析，以推荐更好的解决方案。