###### Abstract
Such a robotic system interacts with the user to learn the user's preferences, with the aim of providing a better service, and the user or any authorised users should be able to interact with the robotic system locally or remotely. In the process, this poses several unanswered questions pertaining to users' privacy and data security: What if the input data is manipulated? What if the stored data is either visible to, or corrupted or tampered with by an unauthorised user? What if the sensor data is fraudulent? What if the robot is accessed by unauthorised users? What if the robot and the sensory devices are hijacked by hackers? If connected remotely, how to approve users, control access, and limit their rights, and how security keys are managed, approved, and authorised. What about the user's privacy? Is the user's data leaked during communication and interaction unintentionally to unauthorised users? Where is the user's data stored and managed? What if malware is \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Region**} & **Number of Persons Aged 65** & **Number of Persons Aged 65** & **Percentage Change between** \\  & **or Over in 2019 (Millions)** & **or Over in 2050 (Millions)** & **2019 and 2050** \\ \hline World & 702.9 & 1548.9 & 120 \\ \hline Sub-Saharan Africa & 31.9 & 101.4 & 218 \\ \hline Northern Africa and Western Asia & 29.4 & 95.8 & 226 \\ \hline Central and Southern Asia & 119.0 & 328.1 & 176 \\ \hline Eastern and South-Eastern Asia & 260.6 & 572.5 & 120 \\ \hline Latin America and the Caribbean & 56.4 & 144.6 & 156 \\ \hline Australia and New Zealand & 4.80 & 8.80 & 84 \\ \hline Oceania, excluding Australia and New Zealand & 0.50 & 1.50 & 190 \\ \hline Europe and Northern America & 200.4 & 296.2 & 48 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Source: United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019.  
running and controlling the system? What if a man-in-middle is accessing and controlling the robot? Thus, apart from acceptance and functionality for the robotic system, security and privacy preservation is important, so that it is safe to adopt. In addition, transparency of the robot's decision making is equally critical, so that the user knows exactly when, what, and why the decisions are taken by the robotic system, to gain the user's trust. Therefore, this article focuses on the transparency, privacy, and security issues of the robot, its user, its sensors, and its data. In order to address the transparency challenges of the robot, private permission-based blockchain technology is a potential solution for incorporating visibility and traceability to the authorised users. However, designing a scalable and energy-efficient blockchain framework is a research domain to explore, as blockchain is resource hungry and computation-intensive in nature. On the other hand, the robot's engagement should be real-time and computation overheads should not harm the robotics' real-time performance. The following sections cover the types of robots; detailed analysis of the state-of-the-art literature; and an in-depth study of transparency, security, and privacy requirements; as well as issues and challenges in a multimodal robotic system; followed by prospective solutions to make a system secure by design. The examples given in the following sections were selected based on their significance to the problem that we are analysing.