arXiv subjects

Subodh Vishnu Sharma

Publications and source records attributed to Subodh Vishnu Sharma.

2 recordsLinked to original sources

arXiv2024

Publicly auditable privacy-preserving electoral rolls

While existing literature on electronic voting has extensively addressed verifiability of voting protocols, the vulnerability of electoral rolls in large public elections remains a critical concern. To ensure integrity of electoral rolls, the current practice is to either make electoral rolls public or share them with the political parties. However, this enables construction of detailed voter profiles and selective targeting and manipulation of voters, thereby undermining the fundamental principle of free and fair elections. In this paper, we study the problem of designing publicly auditable yet privacy-preserving electoral rolls. We first formulate a threat model and provide formal security definitions. We then present a protocol for creation, maintenance and usage of electoral rolls that mitigates the threats. Eligible voters can verify their inclusion, whereas political parties and auditors can statistically audit the electoral roll. Further, the audit can also detect polling-day ballot stuffing and denials to eligible voters by malicious polling officers. The entire electoral roll is never revealed, which prevents any large-scale systematic voter targeting and manipulation.

cs.CR↗

arXiv2023

Efficient Adversarial Input Generation via Neural Net Patching

The generation of adversarial inputs has become a crucial issue in establishing the robustness and trustworthiness of deep neural nets, especially when they are used in safety-critical application domains such as autonomous vehicles and precision medicine. However, the problem poses multiple practical challenges, including scalability issues owing to large-sized networks, and the generation of adversarial inputs that lack important qualities such as naturalness and output-impartiality. This problem shares its end goal with the task of patching neural nets where small changes in some of the network's weights need to be discovered so that upon applying these changes, the modified net produces the desirable output for a given set of inputs. We exploit this connection by proposing to obtain an adversarial input from a patch, with the underlying observation that the effect of changing the weights can also be brought about by changing the inputs instead. Thus, this paper presents a novel way to generate input perturbations that are adversarial for a given network by using an efficient network patching technique. We note that the proposed method is significantly more effective than the prior state-of-the-art techniques.

cs.LG↗