# Personalized Context-Aware Authentication Protocols in IoT
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Sensors** & **Minimal Computational** & **Contactless** & **Uniqueness Guarantee** & **Privacy** & **Rating** & **Commentary** \\  & **Accessibility** & **for IoT** & **Pour** & & **Guarantee** & **Assured** & **Rating** & **Commentary** \\ \hline Fingerprints & 1 & 1 & 0 & 1 & 0 & 3 & Ubiquitous and unique, but \\ Retina & 0 & 0 & 0 & 1 & 0 & 1 & Invasive and costly \\ Iris & 1 & 0 & 1 & 1 & 0 & 3 & Easy to implement but depends \\ Emotions/EEG & 0 & 0 & 0 & 0 & 1 & 1 & Hard to implement and \\ Imprinted neural & 0 & 1 & 0 & 0 & 1 & 2 & Needs training \\ patterns & 0 & 0 & 0 & 0 & 1 & 1 & Specialized for cars \\ Sitting posture & 1 & 1 & 0 & 0 & 1 & 3 & Easy to implement, has a \\ Typing cadence & 1 & 1 & 1 & 0 & 0.5 & 3.5 & Easy to implement, may access \\ Eye movement & 1 & 1 & 1 & 0 & 0.5 & 3.5 & Private data \\ Cognitive & 1 & 1 & 1 & 1 & 0.5 & 4.5 & A step above eye recognition, \\ cryptography & & & & & & & may still access private data \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of features for selected personal patterns and their usefulness. As can be seen in the above results, the most considerable risk, in this case, is personal privacy. A lot of the algorithm's strengths and weaknesses depend on the dataset of an individual, which might include just general data or operate on his personal history and biomedical scans (e.g., fitness tracker records) on as deep a level as there is consent to use. Thus, it has been rated as \(+/-\)[17]. Given the nature of the context-aware algorithm, the data had to be carefully selected and curated. Thus, for the initial data vector usage, the following were considered:  
* Fingerprints are one of the most ubiquitous forms of IoT access control. Sensors are widely used. * Iris recognition is less popular but is still easy to use with facial and image recognition. * Face recognition (see above) is an important part of biometrics. It does not guarantee uniqueness but needs to provide context, i.e., for user photos. * Voice recognition is certainly not unique but is readily available and easy to capture and process on the fly. It is effective when paired with other methods [18]. * Speech patterns go hand in hand with voice recognition, giving context to a voice sample. * Typing cadence is similar to speech patterns but is the equivalent for devices with keyboards. * GPS data are only usable for providing user context but are readily available in a wide range of devices. * Sleep patterns and ECG data are available from IoT fitness and health devices. They are classified as confidential health data. * Scanned written signatures (or other handwriting samples) are used for context purposes. * Social media data (freely available) are used by devices with social media access, using only publicly available information (freely shared by the authenticated person for public use). * Social media data (with the individual's consent)--usage of private social media data and history, acknowledged and permitted by the device owner. * Device usage history (i.e., browser history, time of access, etc., also needs consent) includes non-social data from web activity tracking engines or the device itself. * Personal photos and media are highly confidential and require sign-off from the individual in question, but they are the best material for context generation. Ideally, there is a locally stored archive in the local network (i.e., an NAS connected to the smart home system) that consists of a collection of user data and his 'digital footprint'. As can be seen, the data that can be used for authentication purposes form a vast spectrum. There are at least two caveats: in the case of private data, all the waivers or legal agreements (EULAs--end-user license agreements) have to be read and signed by the individual, and the appropriate standards of storing such data have to be observed as per the local regulations.