# The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey
## 4 Fog-Enabled IoT Applications Security Requirements
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Threat** & **Description** \\ \hline \multirow{2}{*}{Forgery [44]} & Fake identities and profiles, fake information to mislead the user. Saturate resource consumption through fake data. That is, in E-Health and home automation systems, one can easily fake their identifications and profiles to generate any attack. \\ \hline \multirow{2}{*}{Tampering [45]} & Degrading the efficiency of fog by dropping/delay transmitting data. That is, energy conservation systems are responsible to collect the data related to electricity supply, consumption patterns, smart metering, pricing and other details. As the data are very critical, dropping or delaying the data may cause problems. \\ \hline \multirow{2}{*}{Spamming [46]} & Spreading redundant information which causes to consume resources unnecessarily. The attack generated on smart cities lies in this domain. \\ \hline \multirow{2}{*}{Sybil [47]} & Legitimate user personal information and manipulation of fake identities to take over the illegal control on fog resources. That is, in smart home and smart cities, legitimate user can manipulate the fake identities to take control of the network. \\ \hline \multirow{2}{*}{Jamming [48]} & Jam communication network by spreading burst if dummy data on the network. Any type of smart environment can be attacked by Jamming. \\ \hline \multirow{2}{*}{Eavesdropping [49]} & Capturing of transmitting packets and try to read the contents. Any type of smart environment can be a victim of these attacks. \\ \hline \multirow{2}{*}{DoS [50]} & Flooding of superfluous requests to fog nodes to disrupt the services for users. The data generated by smart cities and smart agriculture can be a victim of DoS and flooding attacks. \\ \hline \multirow{2}{*}{Collusion} & Acquiring unfair advantage through deceiving, misleading and defrauding legal entities by collusion of two or more parties. \\ \hline \multirow{2}{*}{Man-In-The-Middle [50]} & Involving between two parties and manipulate exchanged data between them. E-Health and Smart cities are the best fit examples. \\ \hline \multirow{2}{*}{Impersonation [51]} & Pretending the fake services as fog services to the users. \\ \hline \multirow{2}{*}{Identity Privacy [52]} & User personal information leakage such as phone number, visa number, etc. on a communication channel. \\ \hline \multirow{2}{*}{Data Privacy [52]} & Exposure of user data to unreliable parties considerably reaches to privacy leakage. Smart homes, smart cities and E-Health systems are commonly known victim of these types of attacks. \\ \hline \multirow{2}{*}{Usage Privacy [52]} & Leakage of services utilization pattern of users. \\ \hline \multirow{2}{*}{Location Privacy [52]} & Capturing user’s location information to expose or observe user moments. Smart homes, smart cities and E-Health systems are commonly known victims of these types of attacks. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Security and privacy threats in IoT-enabled applications.