arXiv subjects

Ashok Singh Sairam

Publications and source records attributed to Ashok Singh Sairam.

3 recordsLinked to original sources

arXiv2025

Dimension Reduction via Random Projection for Privacy in Multi-Agent Systems

In a Multi-Agent System (MAS), individual agents observe various aspects of the environment and transmit this information to a central entity responsible for aggregating the data and deducing system parameters. To improve overall efficiency, agents may append certain private parameters to their observations. For example, in a crowd-sourced traffic monitoring system, commuters might share not only their current speed, but also sensitive information such as their location to enable more accurate route prediction. However, sharing such data can allow the central entity or a potential adversary to infer private details about the user, such as their daily routines. To mitigate these privacy risks, the agents sanitize the data before transmission. This sanitization inevitably results in a loss of utility. In this work, we formulate the problem as a utility-privacy trade-off and propose a novel compression-based approach leveraging the notion of robust concepts to sanitize the shared data. We further derive a bound on the norm of the compression matrix required to ensure maximal privacy while satisfying predefined utility constraints.

cs.CR↗

arXiv2019

A Survey of Controller Placement Problem in Software Defined Networks

Software Defined Network (SDN) is an emerging network paradigm which provides a centralized view of the network by decoupling the network control plane from the data plane. This strategy of maintaining a global view of the network optimizes resource management. However, the implementation of SDN using a single physical controller lead to issues of scalability and robustness. A physically distributed but logically centralized SDN controller architecture promises to resolve both these issues. Distributed SDN along with its benefits brings along the problem of the number of controllers required and their placement in the network. This problem is referred to as the controller placement problem (CPP) and this paper is mainly concerned with the CPP solution techniques. The paper formally defines CPP, gives a comprehensive review of the various performance metrics and characteristics of the available CPP solutions. Finally, we point out the existing literature gap and discuss the future research direction in this domain.

cs.NI↗

arXiv2017

Modeling and Performance Comparison of Privacy Approaches for Location Based Services

In pervasive computing environment, Location Based Services (LBSs) are getting popularity among users because of their usefulness in day-to-day life. LBSs are information services that use geospatial data of mobile device and smart phone users to provide information, entertainment and security in real time. A key concern in such pervasive computing environment is the need to reveal the user's exact location which may allow an adversary to infer private information about the user. To address the privacy concerns of LBS users, a large number of security approaches have been proposed based on the concept of k-anonymity. The central idea in location k-anonymity is to find a set of k-1 users confined in a given geographical area of the actual user, such that the location of these k users are indistinguishable from one another, thus protecting the identity of the user. Although a number of performance parameters like success rate, amount of privacy achieved are used to measure the performance of the k-anonymity approaches, they make the implicit, unrealistic assumption that the k-1 users are readily available. As such these approaches ignore the turnaround time to process a user request, which is crucial for a real-time application like LBS. In this work, we model the k-anonymity approaches using queuing theory to compute the average sojourn time of users and queue length of the system. To demonstrate that queuing theory can be used to model all k-anonymity approaches, we consider graph-based k-anonymity approaches. The proposed analytical model is further validated with experimental results.

cs.CR↗