Anne V. D. M. Kayem

Are motorways rational from slime mould's point of view?

We analyse the results of our experimental laboratory approximation of motorway networks with slime mould Physarum polycephalum. Motorway networks of 14 geographical areas are considered: Australia, Africa, Belgium, Brazil, Canada, China, Germany, Iberia, Italy, Malaysia, Mexico, the Netherlands, UK and USA. For each geographical entity, we represented major urban areas by oat flakes and inoculated the slime mould in a capital. After slime mould spanned all urban areas with a network of its protoplasmic tubes, we extracted a generalised Physarum graph from the network and compared the graphs with an abstract motorway graph using most common measures. The measures employed are the number of independent cycles, cohesion, shortest paths lengths, diameter, the Harary index and the Randić index. We obtained a series of intriguing results, and found that the slime mould approximates best of all the motorway graphs of Belgium, Canada and China, and that for all entities studied the best match between Physarum and motorway graphs is detected by the Randić index (molecular branching index).

Secure and Reliable Power Consumption Monitoring in Untrustworthy Micro-grids

Micro-grid architectures based on renewable energy sources offer a viable solution to electricity provision in regions that are not connected to the national power grid or that are severely affected by load shedding. The limited power generated in micro-grids however makes monitoring power consumption an important consideration in guaranteeing efficient and fair energy sharing. A further caveat is that adversarial data tampering poses a major impediment to fair energy sharing on small scale energy systems, like micro-grids, and can result in a complete breakdown of the system. In this paper, we present an innovative approach to monitoring home power consumption in smart micro-grids. This is done by taking into account power consumption measurement on a per appliance and/or device basis. Our approach works by employing a distributed snapshot algorithm to asynchronously collect the power consumption data reported by the appliances and devices. In addition, we provide a characterization of noise that affects the quality of the data making it difficult to differentiate measurement errors and power fluctuations from deliberate attempts to misreport consumption.

Efficient enforcement of dynamic cryptographic access control policies for outsourced data

Outsourcing of their data to third-party service providers is a cost-effective data management strategy for many organizations. Outsourcing, however, introduces new challenges with respect to ensuring the security and the privacy of the data. In addition to the need for standard access control policies, organizations must now be concerned with the privacy of their data and so hiding the data from the service provider is important. Simply encrypting the data before it is transmitted to the service provider is inefficient and vulnerable to security attacks when the access control policies change. Approaches based on two layers of encryption alleviate the privacy concern but still require re-encryption of the data when policies change. This paper presents a novel and efficient solution that employs two layers of encryption of the data and an encrypted data object containing the second access key. Changes to the access control policies are handled by re-encrypting the object containing the affected key, which is an efficient operation. The paper presents our key management approach, a security analysis of our approach, and an evaluation of the performance of a proof of concept implementation of our approach.

Fault-tolerant Distributed Continuous Double Auctioning on Computationally Constrained Microgrids

In this article we show that a mutual exclusion protocol supporting continuous double auctioning for power trading on computationally constrained microgrid can be fault tolerant. Fault tolerance allows the CDA algorithm to operate reliably and contributes to overall grid stability and robustness. Contrary to fault tolerance approaches proposed in the literature which bypass faulty nodes through a network reconfiguration process, our approach masks crash failures of cluster head nodes through redundancy. Masking failure of the main node ensures the dependent cluster nodes hosting trading agents are not isolated from auctioning. A rendundant component acts as a backup which takes over if the primary components fails, allowing for some fault tolerance and a graceful degradation of the network. Our proposed fault-tolerant CDA algorithm has a complexity of O(N) time and a check-pointing message complexity of O(W ). N is the number of messages exchanged per critical section. W is the number of check-pointing messages.

A Distributed Continuous Double Auction Framework for Resource Constrained Microgrids

Microgrids are power networks which may operate autonomously or in parallel with national grids and the ability to function in case of islanding events, allowing critical national infrastructures to be both more efficient and robust. Particularly at smaller scales and when relying on renewable energy, stability of microgrids is critical. In this paper we propose a token-based CDA algorithm variant which may be frequently run on resource-constrained devices to efficiently match loads and generator capacity. The new algorithm was proven theoretically that it satisfies the mutual exclusion properties, while yielding an acceptable time and message complexity of \(\mathscr {O}(N)\) and \(\mathscr {O}(\log N)\) respectively. The algorithm should generally be compatible to microgrids supported by a hierarchical network topology where households form cluster nodes around a single smart meter-cluster head (a setup similar to the one discussed in Sect. 3).

Power Auctioning in Resource Constrained Micro-grids: Cases of Cheating

In this paper, we consider the Continuous Double Auction (CDA) scheme as a comprehensive power resource allocation approach on micro-grids. Users of CDA schemes are typically self-interested and so work to maximize self-profit. Meanwhile, security in CDAs has received limited attention, with little to no theoretical or experimental evidence demonstrating how an adversary cheats to gain excess energy or derive economic benefits. We identify two forms of cheating realised by changing the trading agent (TA) strategy of some of the agents in a homogeneous CDA scheme. In one case an adversary gains control and degrades other trading agents’ strategies to gain more surplus. While in the other, K colluding trading agents employ an automated coordinated approach to changing their TA strategies to maximize surplus power gains. We propose an exception handling mechanism that makes use of allocative efficiency and message overheads to detect and mitigate cheating forms.

K-Anonymity for Privacy Preserving Crime Data Publishing in Resource Constrained Environments

Mobile crime report services have become a pervasive approach to enabling community-based crime reporting (CBCR) in developing nations. These services hold the advantage of facilitating law enforcement when resource constraints make using standard crime investigation approaches challenging. However, CBCRs have failed to achieve widespread popularity in developing nations because of concerns for privacy. Users are hesitant to make crime reports with out strong guarantees of privacy preservation. Furthermore, oftentimes lack of data mining expertise within the law enforcement agencies implies that the reported data needs to be processed manually which is a time-consuming process. In this paper we make two contributions to facilitate effective and efficient CBCR and crime data mining as well as to address the user privacy concern. The first is a practical framework for mobile CBCR and the second, is a hybrid k-anonymity algorithm to guarantee privacy preservation of the reported crime data. We use a hierarchy-based generalization algorithm to classify the data to minimize information loss by optimizing the nodal degree of the classification tree. Results from our proof-of-concept implementation demonstrate that in addition to guaranteeing privacy, our proposed scheme offers a classification accuracy of about 38% and a drop in information loss of nearly 50% over previous schemes when compared on various sizes of datasets. Performance-wise we observe an average improvement of about 50ms proportionate to the size of the dataset.

Enhancing identity trust in cryptographic key management systems for dynamic environments

Cryptographic key management (CKM) schemes can be used to support identity management (IM) systems where linking users securely to data objects is important. CKM schemes enforce data security by encrypting data granting access only to authorized users and security compromises are prevented by updating any keys that are held by users from whom access rights have been revoked. Handling key updates efficiently and providing security against collusion attacks is challenging in dynamic environments like the Internet where manual Security management increases the likelihood of delayed responses. Delay increases the systems vulnerability to security attacks and the potential of the systems violating its service level agreements. Adaptive CKM has emerged as a possibility of addressing this problem but needs to be designed in a way that justifies the cost/benefit tradeoff. In this paper, we show that the key update and collusion avoidance problems are NP-complete and need heuristic algorithms to prevent performance degradations in comparison to standard CKM schemes. As an example of the benefits of a good heuristic, we present a collusion detection and resolution algorithm whose running time is polynomial in the number of keys. The algorithm operates by mapping the generated key set onto a key graph whose independent set is computed. In the key graph, the vertices represent the keys and the edges the probability that their endpoints can be combined to provoke a collusion attack. Collusion possibilities are resolved by applying a heuristic that resets the probability to zero. The performance of our algorithm is analyzed in comparison to the Akl and Taylor scheme that is secure against collusion attack, and the experimental results indicate that collusion prevention can be done dynamically without affecting performance. Copyright

On monitoring information flow of outsourced data

Data outsourcing is an Internet-based paradigm that allows organizations to share data cost-effectively by transferring data to a third-party service provider for management. Enforcing outsourced data privacy in untrustworthy environments is challenging because the data needs to be kept secret both from unauthorized users and the service provider (SP). Existing approaches propose that the data owner(s) encrypt the data before it is transferred to the service provider to preserve confidentiality. Access is only granted to a user initiated program if the key presented can decrypt the data into a readable format. Therefore the data owner can control access to the data without having to worry about the management costs. However, this approach fails to monitor the data once it has been retrieved from the SPs end. So, a user can retrieve information from the SPs end and share it with unauthorized users or even the SP. We propose a conceptual framework, based on the concept of dependence graphs, for monitoring data exchanges between programs in order to prevent unauthorized access. The framework has a distributed architecture which is suitable for data outsourcing environments and the web in general. Each data object contains a cryptographic tag (like an invisible digital watermark) that is computed by using a cryptographic hash function to combine the checksum of the data and the encryption key. In order to execute an operation with a data object the key presented for decryption must match the one associated with the users role and generate a cryptographic tag that matches the one embedded into the data. Tracing data exchanges, in this way, can leverage data privacy for organizations that transfer data management to third party service providers.

Decentralised Scheduling of Power Consumption in Micro-grids: Optimisation and Security

We consider a micro-grid architecture that is distributed in nature and reliant on renewable energy. In standard grid architectures, demand management is handled via scheduling protocols that are centrally coordinated. Centralised approaches are however computationally intensive, thus not suited to distributed grid architectures with limited computational power. We address this problem with a decentralised scheduling algorithm. In our scheduling algorithm, the alternating direction method of multipliers (ADMM) is used to decompose the scheduling problem into smaller sub problems that are solved in parallel over local computation devices, which yields an optimal solution. We show that ADMM can be used to model a scheduling solution that handles both decentralised and fully decentralised cases. As a further step, we show that false data injection attacks can be provoked by compromising parts of the communication infrastructure or a set of computing devices. In this case, the algorithm fails to converge to an optimum or converges toward a value that lends the attacker an advantage, and impacts the scheduling scheme negatively.