Asim K. Pal

A heuristic method for RCPSP with fuzzy activity times

In this paper, we propose a heuristic method for resource constrained project scheduling problem with fuzzy activity times. This method is based on priority rule for parallel schedule generation scheme. Calculation of critical path in this case requires comparison of fuzzy numbers. Distance based ranking of fuzzy number is used for finding the critical path length and concept of shifting criticality is proposed for some of the special cases. We also propose a measure for finding the non-integer power of a fuzzy number. We discuss some properties of the proposed method. We use an example to illustrate the method.

Privacy preserving collaborative business process management

The basic objective of collaborative supply chain planning (CSCP) is to fulfill the demand of the customers by integrating a network of organizations through mediums such as internet. But, the supply chain (SC) partners are often reluctant to share their strategic information. The exchange of relevant strategic information yet maintaining privacy is a challenging issue for CSCP. It is therefore required to develop privacy preserving coordination mechanisms (PPCM) that can align the business objectives of SC partners. This paper presents a distributed algorithm for PPCM for CSCP based on secure multiparty computation. This requires negotiations for compensation between the buying and the selling firms. We have considered a single buyer and single supplier (SBSS) case along with the process flow logic using a process flow construct for secure computation. We have extended the method to a multiparty negotiation process for a multiple buyer and single supplier (MBSS) case.

On the asymptotic performance of IDA

Since best‐first search algorithms such as A* require large amounts of memory, they sometimes cannot run to completion, even on problem instances of moderate size. This problem has led to the development of limited‐memory search algorithms, of which the best known is IDA*. This paper presents the following results about IDA* and related algorithms:1) The analysis of asymptotic optimality for IDA* in [R.E. Korf, Optimal path finding algorithms, in: Search in Artificial Intelligence, eds. L. Kanal and V. Kumar (Springer‐Verlag, 1988) pp. 200-222] is incorrect. There are trees satisfying the asymptotic optimality conditions given in [R.E. Korf, Optimal path finding algorithms, in: Search in Artificial Intelligence, eds. L. Kanal and V. Kumar (Springer‐Verlag, 1988) pp. 200-222] for which IDA* is not asymptotically optimal.2) To correct the above problem, we state and prove necessary and sufficient conditions for asymptotic optimality of IDA* on trees. On trees not satisfying our conditions, we show that no best‐first limited‐memory search algorithm can be asymptotically optimal.3) On graphs, IDA* can perform quite poorly. In particular, there are graphs on which IDA* does Ω(22N) node expansions where N is the number of nodes expanded by A*.

A Policy-Based Security Framework for Storage and Computation on Enterprise Data in the Cloud

A whole range of security concerns that can act as barriers to the adoption of cloud computing have been identified by researchers over the last few years. While outsourcing its business-critical data and computations to the cloud, an enterprise loses control over them. How should the organization decide what security measures to apply to protect its data and computations that have different security requirements from a Cloud Service Provider (CSP) with an unknown level of corruption? The answer to this question relies on the organizations perception about the CSPs trustworthiness and the security requirements of its data. This paper proposes a decentralized, dynamic and evolving policy-based security framework that helps an organization to derive such perceptions from knowledgeable and trusted employee roles and based on that, choose the most relevant security policy specifying the security measures necessary for outsourcing data and computations to the cloud. The organizational perception is built through direct user participation and is allowed to evolve over time.

Learning with an Embedded Reject Option

The option to reject an example in order to avoid the risk of a costly potential misclassification is well-explored in the pattern recognition literature. In this paper, we look at this issue from the perspective of statistical learning theory. Specifically, we look at ways of modeling the problem of learning with an embedded reject option, in terms of minimizing an appropriately defined risk functional, and discuss the applicability thereof of some fundamental principles of learning, such as minimizing empirical risk and structural risk. Finally, we present some directions for further theoretical work on this problem.

Privacy preserving decentralized method for computing a pareto-optimal solution

Distributed methodologies to find pareto-optimal frontier with concern to privacy, of objectives and constraints, of parties is of interest in scenarios like negotiations. Adaptation of lagrangian method to solve distributed weighting method for both strictly concave and not strictly concave (e.g. linear) value functions is proposed for a maximization problem.

Finding pareto-optimal set of distributed vectors with minimum disclosure

A set of alternatives along with their associated values is available with individual parties who do not want to share it with others. Algorithms to find the feasible set (i.e. common subset) X and the pareto-optimal subset of X with minimum disclosure in presence or absence of a third party are explored. These are useful in multiparty negotiations.

Cloud-Based Privacy Aware Preference Aggregation Service

Each day newer security and privacy risks are emerging in the online world. Users are often wary of using online services because they are not entirely confident of the level of security the provider is offering, particularly when such services may involve monetary transactions. Often the level of security in the algorithms underlying online and cloud-based services cannot be controlled by the user but is decided by the service provider. We propose a cloud-based Privacy Aware Preference Aggregation Service (PAPAS) that enables users to match preferences with other interested users of the service to find partners for negotiation, peer-groups with similar interests etc while also allowing users the ability to decide the level of security desired from the service, especially with respect to correct output and privacy of inputs of the protocol. It also lets users express their level of trust on the provider enabling or disabling it to act as a mediating agent in the protocols. Along with this we analyze the security of a preference hiding algorithm in the literature based on the security levels we propose for the PAPAS framework and suggest an improved version of the multi-party privacy preserving preference aggregation algorithm that does not require a mediating agent.

Privacy-Preserving 1-n-p Negotiation Protocol

In this paper, we have designed an electronic market where a buyer negotiates with n suppliers to procure p types of items within a given time frame. A privacy preserving 1-n-p negotiation protocol has been developed based on secure group communication and secure multiparty computation. The suppliers submit their bids. The objective is to label the bids as winning or losing so as to minimize the buyers cost with the constraint that the buyer obtains all items in required quantity. The negotiation process has two distinct phases - pre-bid and final bid. During pre-bid phase, the suppliers singly or jointly bid for a combination of items. The privacy requirements considered are: a) pre-bid: forward and backward privacy and anonymity of the winner in each pre-bid cycle, b) final bid: anonymity of the losers and traceability of the winners.

Discovering Conversational Topics and Emotions Associated with Demonetization Tweets in India

Social media platforms, owing to its great wealth of information, facilitates one’s opportunities to explore hidden patterns or unknown correlations. It also finds its credibility in understanding people’s expressions from what they are discussing on online platforms. As one showcase, in this paper, we summarize the dataset of Twitter messages related to recent demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights from Twitter’s data. Our proposed system automatically extracts the popular latent topics in conversations regarding demonetization discussed in Twitter via the Latent Dirichlet Allocation (LDA)-based topic model and also identifies the correlated topics across different categories. Additionally, it also discovers people’s opinions expressed through their tweets related to the event under consideration via the emotion analyzer. The system also employs an intuitive and informative visualization to show the uncovered insight. Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI), to select the best LDA models. The obtained LDA results show that the tool can be effectively used to extract discussion topics and summarize them for further manual analysis.