Mohamed Shehab

Collective privacy management in social networks

Social Networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of people participating. Social networks enable a form of self expression for users, and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social Network sites, Social Networks yet do not support any mechanism for collaborative management of privacy settings for shared content. In this paper, we model the problem of collaborative enforcement of privacy policies on shared data by using game theory. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness, and that rewards users who promote co-ownership. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge this is the first time such a protection mechanism for Social Networking has been proposed. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of todays most popular social networks. We show that supporting these type of solutions is not also feasible, but can be implemented through a minimal increase in overhead to end-users.

Social-Networks Connect Services

New services such as Facebook Platform, Google Friend Connect, and MySpacelD let third-party sites develop social applications without having to build their own social network. These social-networks connect services increase access to and enrich user data in the Social Web, although they also present several security and privacy challenges.

Watermarking Relational Databases Using Optimization-Based Techniques

Proving ownership rights on outsourced relational databases is a crucial issue in todays internet-based application environments and in many content distribution applications. In this paper, we present a mechanism for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. We formulate the watermarking of relational databases as a constrained optimization problem and discuss efficient techniques to solve the optimization problem and to handle the constraints. Our watermarking technique is resilient to watermark synchronization errors because it uses a partitioning approach that does not require marker tuples. Our approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. We implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration, and insertion attacks.

Privacy policies for shared content in social network sites

Social networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of subscribed users. Social networks enable a form of self-expression for users and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social network sites, they do not provide any mechanisms for collective management of privacy settings for shared content. In this paper, using game theory, we model the problem of collective enforcement of privacy policies on shared data. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness and that rewards users who promote co-ownership. Our approach enables social network users to compose friendship based policies based on distances from an agreed upon central user selected using several social networks metrics. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge, this is the first time such a privacy protection mechanism for social networking has been proposed. We also extend our mechanism so as to support collective enforcement across multiple social network sites. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today’s most popular social networks. Through our implementation, we show the feasibility of such approach and show that it can be implemented with a minimal increase in overhead to end-users. We complete our analysis by conducting a user study to investigate users’ understanding of co-ownership, usefulness and understanding of our approach. Users responded favorably to the approach, indicating a general understanding of co-ownership and the auction, and found the approach to be both useful and fair.

Secure collaboration in mediator-free environments

The internet and related technologies have made multidomain collaborations a reality. Collaboration enables domains to effectively share resources; however it introduces several security and privacy challenges. Managing security in the absence of a central mediator is even more challenging. In this paper, we propose a distributed secure interoperability framework for mediator-free collaboration environments. We introduce the idea of secure access paths which enables domains to make localized access control decisions without having global view of the collaboration. We also present a path authentication technique for proving path authenticity. Furthermore, we present both a proactive and on-demand path discovery algorithms that enable domains to securely discover paths in the collaboration environment.

Privacy-Enhanced User-Centric Identity Management

User-centric identity management approaches have received significant attention for managing private and critical identity attributes from the users perspective. User-centric identity management allows users to control their own digital identities. Users are allowed to select their credentials when responding to an authentication or attribute requester and it gives users more rights and responsibility over their identity information. However, current user-centric approaches mainly focus on interoperable architectures between existing identity management systems and privacy issues have not been considered in depth. In this paper, we propose a category-based privacy preference approach to enhance the privacy of user-centric identity management systems. In addition, we present our proof-of-concept prototype of our approach in the Identity Metasystem.

SERAT: SEcure role mApping technique for decentralized secure interoperability

Multi-domain application environments where distributed domains interoperate with each other are becoming a reality in internet-based and web-services based enterprise applications. The secure interoperation in a multidomain environment is a challenging problem. In this paper, we propose a distributed secure interoperability protocol that ensures secure interoperation of the multiple collaborating domains without compromising the security of collaborating domains. We introduce the idea of access paths and access paths constraints. Furthermore, we device a path discovery algorithm that is capable of querying interoperating domains for the set of secure access paths between different domains.

Secure Provenance Transmission for Streaming Data

Many application domains, such as real-time financial analysis, e-healthcare systems, sensor networks, are characterized by continuous data streaming from multiple sources and through intermediate processing by multiple aggregators. Keeping track of data provenance in such highly dynamic context is an important requirement, since data provenance is a key factor in assessing data trustworthiness which is crucial for many applications. Provenance management for streaming data requires addressing several challenges, including the assurance of high processing throughput, low bandwidth consumption, storage efficiency and secure transmission. In this paper, we propose a novel approach to securely transmit provenance for streaming data (focusing on sensor network) by embedding provenance into the interpacket timing domain while addressing the above mentioned issues. As provenance is hidden in another host-medium, our solution can be conceptualized as watermarking technique. However, unlike traditional watermarking approaches, we embed provenance over the interpacket delays (IPDs) rather than in the sensor data themselves, hence avoiding the problem of data degradation due to watermarking. Provenance is extracted by the data receiver utilizing an optimal threshold-based mechanism which minimizes the probability of provenance decoding errors. The resiliency of the scheme against outside and inside attackers is established through an extensive security analysis. Experiments show that our technique can recover provenance up to a certain level against perturbations to inter-packet timing characteristics.

Access control for online social networks third party applications

With the development of Web 2.0 technologies, online social networks are able to provide open platforms to enable the seamless sharing of profile data to enable public developers to interface and extend the social network services as applications. At the same time, these open interfaces pose serious privacy concerns as third party applications are usually given access to the user profiles. Current related research has focused on mainly user-to-user interactions in social networks, and seems to ignore the third party applications. In this paper, we present an access control framework to manage third party applications. Our framework is based on enabling the user to specify the data attributes to be shared with the application and at the same time be able to specify the degree of specificity of the shared attributes. We model applications as finite state machines, and use the required user profile attributes as conditions governing the application execution. We formulate the minimal attribute generalization problem and we propose a solution that maps the problem to the shortest path problem to find the minimum set of attribute generalization required to access the application services. We assess the feasibility of our approach by developing a proof-of-concept implementation and by conducting user studies on a widely-used social network platform.

A Lightweight Secure Scheme for Detecting Provenance Forgery and Packet DropAttacks in Wireless Sensor Networks

Large-scale sensor networks are deployed in numerous application domains, and the data they collect are used in decision-making for critical infrastructures. Data are streamed from multiple sources through intermediate processing nodes that aggregate information. A malicious adversary may introduce additional nodes in the network or compromise existing ones. Therefore, assuring high data trustworthiness is crucial for correct decision-making. Data provenance represents a key factor in evaluating the trustworthiness of sensor data. Provenance management for sensor networks introduces several challenging requirements, such as low energy and bandwidth consumption, efficient storage and secure transmission. In this paper, we propose a novel lightweight scheme to securely transmit provenance for sensor data. The proposed technique relies on in-packet Bloom filters to encode provenance. We introduce efficient mechanisms for provenance verification and reconstruction at the base station. In addition, we extend the secure provenance scheme with functionality to detect packet drop attacks staged by malicious data forwarding nodes. We evaluate the proposed technique both analytically and empirically, and the results prove the effectiveness and efficiency of the lightweight secure provenance scheme in detecting packet forgery and loss attacks.