Cloud-based multimedia systems are becoming increasingly common. These systems offer not only storage facility, but also high-end computing infrastructure which can be used to process data for various analysis tasks ranging from low-level data quality enhancement to high-level activity and behavior identification operations. However, cloud data centers, being third party servers, are often prone to information leakage, raising security and privacy concerns. In this research, we present a Shamir's secret sharing based method to enhance the quality of encrypted image data over cloud. Using the proposed method we show that several image enhancement operations such as noise removal, anti-aliasing, edge and contrast enhancement, and dehazing can be performed in encrypted domain with near-zero loss in accuracy and minimal computation and data overhead. Moreover, the proposed method is proven to be information theoretically secure. Further details and demos can be found here.

Using social networks??? Your privacy could be at stake... Here is the solution....

The use of social networks has grown exponentially in recent years, and these social networks continue to have an ever-increasing impact on human lives. There are many concerns regarding the privacy of users in these environments, such as how trustworthy the social network operators are, in addition to the external adversaries. We propose a new architecture for online social networking, based on distributed cloud-based datacenters and using Shamir's secret sharing as the method of encrypting user profile data, for enhanced privacy and availability.

This research focuses on using social network analysis and content textual features to detect the cyber bullying cases. First, based on related literature from social science and psychology, we built three hypotheses for the differences between cyber bullying and non-bullying cases. Then, by adding 1.5 ego-network graphs from two users in a conversation, we can build and learn the relationship graph of these two users. After gathering the social network data, such as relationship tie, number of friends, and social network centrality, we find that multiple social characteristics are statistically different between the cyber bullying and non-bullying groups, thus supporting the results found in previous psychological studies. Finally, the cyber bullying detection models have been significant improved by adding social network features compared with only using textual features.
For more details, please click here.

The higher the number of share (hidden image/video) holders, the higher the chances of the secret image/video being seen...
Traditional secret image sharing methods have an all-or-nothing property, which is not suitable for applications which require gradual reconstruction. In this paper, we propose a scalable secret image sharing (SSIS) method that provides gradual reconstruction with smooth scalability. Furthermore, we extend this method to videos and propose a scalable secret video sharing (SSVS) method. These two methods are designed for compressed multimedia (i.e. JPEG images and H.264 videos). In both methods, the size of the shadow images (shares) is reduced to an optimal value. Experimental results and analyses show that the proposed methods are computationally and semantically secure.
For more details, please click here.

Today digital video is used extensively in many applications. Sometimes a video could be treated as a top secret for an organization, for example military secrets, surveillance footage and corporate product designs, and may need to be shared among a group of people in a secure manner. Traditional data security methods such as encryption techniques are prone to single-point attack, i.e. the secret can be revealed by obtaining the decryption key from any single person. Alternatively, the secret sharing scheme provides collective control over the secrecy of information and is considered information theoretically secure. In this research, we propose to adopt a secret sharing based approach to provide collective control over a given sensitive video. We present three methods that utilize the spatial and temporal redundancy in videos in different ways. We analyze the security of these methods and compare them for efficiency in terms of computation time and space using extensive experimentation.
Data set used for this research has been made public. For more details, please click here.

Be alert, somebody is running and shouting in the corridor..., hmmm... How to assimilate the information from multiple heterogeneous sensors in order to detect such events?
For more details, please see: Information assimilation for event detection (Collaborators: Mohan Kankanhalli, Ramesh Jain, John Oommen, and Namunu Maddage).

Where to place the sensors for effective surveillance...?
For more details, please see: Optimal Sensor Placement (Collaborators: Mohan Kankanhalli, G. Shivaram, Vivek K. Singh and K R Ramakrishnan).

Who has left this item in the hallway...? This might be dangerous... How can sensors actively compete and cooperate with each other to detect the abandoned object, identify the person and his/her movement?
For more details, please see: Active Coopetitive Sensing and Coopetitive Multimedia Surveillance. (Collaborators: Mohan Kankanhalli and Vivek K. Singh).

Be careful... a sleepwalker is walking out of the house during sleep... system should alert the caretaker.
For more details: Sleepwalker Monitoring (Collaborators: Abed El Saddik and Md. Anwar Hossain).

Can you believe...? This is 'Pradeep' addressing United States from Washington DC... OR Is it media tampering...?
For more details, please see: Digital video authentication (Collaborators: Mohan Kankanhalli and Weiqi Yan).

This research advocates for the web sites to have trust levels for various domains. It would allow the web users to see web information along with its trust level. Determining the trust level of the web information will also significantly help in performing trust-based search. We aim to design and develop algorithms to dynamically compute the trust level of a web site for a particular domain based on the past history of semantic agreement/disagreement between this web site and a trusted web site in that domain. (Collaborators: Abed El Saddik, Sheela Ramanna, Hicham Ibrahim and Md. Anwar Hossain).