A survey on membership inference attacks and defenses in machine learning

Membership inference(MI)attacks mainly aim to infer whether a data record was used to train a target model or not.Due to the serious privacy risks,MI attacks have been attracting a tremendous amount of attention in the research community.One existing work conducted-to our best knowledge the first dedicated survey study in this specific area:The survey provides a comprehensive review of the literature during the period of 2017~2021(e.g.,over 100 papers).However,due to the tremendous amount of progress(i.e.,176 papers)made in this area since 2021,the survey conducted by the one existing work has unfortunately already become very limited in the following two aspects:(1)Although the entire literature from 2017~2021 covers 18 ways to categorize(all the proposed)MI attacks,the literature during the period of 2017~2021,which was reviewed in the one existing work,only covered 5 ways to categorize MI attacks.With 13 ways missing,the survey conducted by the one existing work only covers 27%of the landscape(in terms of how to categorize MI attacks)if a retrospective view is taken.(2)Since the literature during the period of 2017~2021 only covers 27%of the landscape(in terms of how to categorize),the number of new insights(i.e.,why an MI attack could succeed)behind all the proposed MI attacks has been significantly increasing since year 2021.As a result,although none of the previous work has made the insights as a main focus of their studies,we found that the various insights leveraged in the literature can be broken down into 10 groups.Without making the insights as a main focus,a survey study could fail to help researchers gain adequate intellectual depth in this area of research.In this work,we conduct a systematic study to address these limitations.In particular,in order to address the first limitation,we make the 13 newly emerged ways to categorize MI attacks as a main focus on the study.In order to address the second limitation,we provide-to our best knowledge-the first review of the various insights leveraged in the entire literature.We found that the various insights leveraged in the literature can be broken down into 10 groups.Moreover,our survey also provides a comprehensive review of the existing defenses against MI attacks,the existing applications of MI attacks,the widely used datasets(e.g.,107 new datasets),and the eva luation metrics(e.g.,20 new evaluation metrics).

Engineering and modification of microbial chassis for systems and synthetic biology

Engineering and modifying synthetic microbial chassis is one of the best ways not only to unravel the fundamental principles of life but also to enhance applications in the health,medicine,agricultural,veterinary,and food industries.The two primary strategies for constructing a microbial chassis are the top-down approach(genome reduction)and the bottom-up approach(genome synthesis).Research programs on this topic have been funded in several countries.The‘Minimum genome factory’(MGF)project was launched in 2001 in Japan with the goal of constructing microorganisms with smaller genomes for industrial use.One of the best examples of the results of this project is E.coli MGF-01,which has a reduced-genome size and exhibits better growth and higher threonine production characteristics than the parental strain[1].The‘cell factory’project was carried out from 1998 to 2002 in the Fifth Framework Program of the EU(European Union),which tried to comprehensively understand microorganisms used in the application field.One of the outstanding results of this project was the elucidation of proteins secreted by Bacillus subtilis,which was summarized as the‘secretome’[2].The GTL(Genomes to Life)program began in 2002 in the United States.In this program,researchers aimed to create artificial cells both in silico and in vitro,such as the successful design and synthesis of a minimal bacterial genome by John Craig Venter's group[3].This review provides an update on recent advances in engineering,modification and application of synthetic microbial chassis,with particular emphasis on the value of learning about chassis as a way to better understand life and improve applications.