SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning

Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an emergent field of research. Recently, PPML has seen a visible shift towards the adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy computation that it entails. In the SOC paradigm, computation is outsourced to a set of powerful and specially equipped servers that provide service on a pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for a range of ML algorithms in SOC setting, that guarantees output delivery to the users irrespective of any adversarial behaviour. Robustness, a highly desirable feature, evokes user participation without the fear of denial of service. At the heart of our framework lies a highly-efficient, maliciously-secure, three-party computation (3PC) over rings that provides guaranteed output delivery (GOD) in the honest-majority setting. To the best of our knowledge, SWIFT is the first robust and efficient PPML framework in the 3PC setting. SWIFT is as fast as (and is strictly better in some cases than) the best-known 3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20) and twice faster than the best-known robust 4PC framework FLASH (Byali et al. PETS'20). We demonstrate our framework's practical relevance by benchmarking popular ML algorithms such as Logistic Regression and deep Neural Networks such as VGG16 and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results testify to our claims that we provide improved security guarantee while incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.Comment: This article is the full and extended version of an article to appear in USENIX Security 202

Measuring Decentralisation in Reforms Era: A Case of Kalyan-Dombivli, India

Since last couple of decades, there is an emerging trend of decentralisation and India is no excep- tion to such a trend. Studies that measure decentralisation in India, however, are mostly comparative and target a limited set of parameters. This paper, attempts at a comprehensive examination of a case of Kalyan -Dombivli (KD), a fringe sub-city to Mumbai. The analysis brings out that over the past seven years (since the beginning of the centrally sponsored urban renewal program), even though the local body in KD had higher resources for local d evelopment, its functional authority, fiscal autonomy, and accountability has been significantly re-centralised towards higher level governments. The case analysis brings out key lessons in terms of need for focusing on the empowerment (functional and fisc al) of local bodies and creating accountability structures that are effective and responsive to the local citizenry

Study of cases with perinatal mortality

Background: Perinatal loss is one of the most traumatic life events. It is indeed a great psychological and emotional shock to not only the mother and father but the entire family and society as a whole. The perinatal mortality rate (PMR) is an important indicator of the quality of obstetric care during pregnancy. Perinatal deaths result largely from obstetric complications that can be prevented with proper antenatal care and quality neonatal services. The study was aims to study the factors related with perinatal loss and its prevention in future pregnancy.Methods: It was a prospective analytical study. All patients with IUFD, stillbirths and early neonatal loss were studied. Postpartum both mother and father were counselled. Detailed history and thorough physical examination were conducted. Data was recorded and tabulated, observation made and compared with results of various studies.Results: The results showed that the incidence of IUFD was 3.7% and early neonatal death was 10.8% per total admissions. The perinatal mortality rate was 63.62 per 1000 live births. Perinatal mortality rate was inversely related to the number of antenatal visits taken by the patient. Lack of antenatal care results in perinatal deaths probably due to failure of early identification and management of maternal problems that impact negatively on perinatal outcome. Even in advanced economies with sophisticated diagnostic and monitoring equipment, lack of antenatal care categorizes a pregnant woman as a high-risk pregnancy.Conclusions: There is a need for awareness regarding importance of antenatal care and institutional delivery. Perinatal mortality is an important indicator of maternal care, health and nutrition. It also reflects the quality of Obstetric and Pediatric care available. Every effort must be made to reduce perinatal mortality

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