India’s “Aadhaar” Biometric ID: Structure, Security, and Vulnerabilities

India\u27s Aadhaar is the largest biometric identity system in history, designed to help deliver subsidies, benefits, and services to India\u27s 1.4 billion residents. The Unique Identification Authority of India (UIDAI) is responsible for providing each resident (not each citizen) with a distinct identity - a 12-digit Aadhaar number - using their biometric and demographic details. We provide the first comprehensive description of the Aadhaar infrastructure, collating information across thousands of pages of public documents and releases, as well as direct discussions with Aadhaar developers. Critically, we describe the first known cryptographic issue within the system, and discuss how a workaround prevents it from being exploitable at scale. Further, we categorize and rate various security and privacy limitations and the corresponding threat actors, examine the legitimacy of alleged security breaches, and discuss improvements and mitigation strategies

Bootstrap Variance Estimation Technique under Dual Frame Ranked Set Sampling

Not AvailableMultiple frames are preferably used when a satisfactory sampling frame, covering the whole population in question, is unavailable or even if such a frame is available it may not be economically advantageous to use that frame for survey because of high cost of sampling per unit. In this paper, we dealt with the problem of variance estimation of the dual frame ranked set sample (DFRSS) estimator. We propose two rescaling Bootstrap variance estimation methods viz. strata based and cluster based, to obtain an unbiased estimator of the sampling variance of the proposed estimator. The comparison of performance of the proposed rescaled bootstrap methods with standard bootstrap methods were investigated through a simulation study. The simulation results show that the proposed methods are more stable and have lesser relative bias than the standard approaches. Among the two rescaling Bootstrap variance estimation methods, the strata based rescaling Bootstrap variance estimation approach is more powerful than its counterpart.Not Availabl