Paper Critique: “Airavat: Security and Privacy for Mapreduce” Essay

1. (10%) resign the problem the paper is trying to solve.This paper is trying to demonstrate how Airavat, a MapReduce-based system of rules for distributed computations provides end-to-end confidentiality, integrity, and seclusion guarantees using a combination of mandatory inlet control and differential gear privacy which provides security and privacy guarantees against selective information leakage.2. (20%) State the main piece of the paper solving a forward-looking problem, proposing a naked as a jaybird algorithm, or presenting a new evaluation (analysis). If a new problem, why was the problem of the essence(p)? Is the problem still important at once? Will the problem be important tomorrow? If a new algorithm or new evaluation (analysis), what are the improvements over former algorithms or evaluations? How do they come up with the new algorithm or evaluation?The main contribution of the paper is that Airavat builds on mandatory advance control (MAC) and differential p rivacy to ensure untrusted MapReduce computations on sensitive information do not leak private information and provide confidentiality, integrity, and privacy guarantees. The goal is to prevent malicious computation providers from violating privacy policies a info provider imposes on the data to prevent leaking information about exclusive items in the data. The system is implemented as a modification to MapReduce and the burnt umber virtual machine, and runs on top of SELinux3. (15%) Summarize the (at most) 3 key main ideas (each in 1 destine.)(1) First work to add MAC and differential privacy to mapreduce. (2) provides a new framework for privacy preserving mapreduce computations. (3) Confines untrusted code.4. (30%) review the main contributiona. Rate the signifi open firece of the paper on a scale of 5 (breakthrough), 4 (significant contribution), 3 (modest contribution), 2 (incremental contribution), 1 (no contribution or negative contribution). Explain your rating in a s entence or two.This system provides security and privacy guarantees for distributed computations on sensitive data at the ends. However, the data still can be leaked in the cloud. Beca expend five-fold machines are involved in the computation and malicious worker can sent the intermediate data to the outside system, which threatens the privacy of the input data. blush not to this extent, temporary data is stored in the workers and those data can be fetched even after computation is done.b. Rate how convincing the methodology is how do the authors justify the resolving power approach or evaluation? Do the authors utilisation arguments, analyses, experiments, simulations, or a combination of them? Do the claims and conclusions follow from the arguments, analyses or experiments? ar the assumptions realistic (at the time of the research)? are the assumptions still valid today? Are the experiments well designed? Are there different experiments that would be much convincing? Are th ere other alternatives the authors should flip considered? (And, of course, is the paper reconcile of methodological errors.)As the authors stated on paginate 3 We aim to prevent malicious computation providers from violating the privacy indemnity of the data provider(s) by leaking information about individual data items. They mapping differential privacy mechanism to ensure this. One interesting solution to data leakage is that they have the mapper specify a divagate of its keys. It seems like that the larger your data set is, the more privacy you have because a user affects less of the output, if removed. They showed results that were really close to 100% with the added noise, it seems this is practicable solution to protect the privacy of your data inputc. What is the most important snareation of the approach?As the authors mention, one computation provider could take in this budget on a dataset for all other computation providers and use more than its fair share. While there is some estimation of effective parameters, there are a large number of parameters that essential(prenominal) be set for Airavat to work properly. This increases the probability of misconfigurations or configurations that might severely limit the computations that can be performed on the data.5. (15%) What lessons should researchers and builders take away from this work. What (if any) questions does this work sacrifice open?The current implementation of Airavat supports both trusted and untrusted Mappers, but Reducers must be trusted and they also modified the JVM to make mappers independent (using deception numbers to identify current and previous mappers). They also modified the reducing agent to provide differential privacy. From the data providers perspective they must provide several privacy parameters like- privacy group and privacy budget.6. (10%) Propose your improvement on the same problem.I have no suggested improvements.