UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning

Training deep neural networks often forces users to work in a distributed or outsourced setting, accompanied with privacy concerns. Split learning aims to address this concern by distributing the model among a client and a server. The scheme supposedly provides privacy, since the server cannot see the clients' models and inputs. We show that this is not true via two novel attacks. (1) We show that an honest-but-curious split learning server, equipped only with the knowledge of the client neural network architecture, can recover the input samples and obtain a functionally similar model to the client model, without being detected. (2) We show that if the client keeps hidden only the output layer of the model to "protect" the private labels, the honest-but-curious server can infer the labels with perfect accuracy. We test our attacks using various benchmark datasets and against proposed privacy-enhancing extensions to split learning. Our results show that plaintext split learning can pose serious risks, ranging from data (input) privacy to intellectual property (model parameters), and provide no more than a false sense of security.Comment: Proceedings of the 21st Workshop on Privacy in the Electronic Society (WPES '22), November 7, 2022, Los Angeles, CA, US

SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learning

Distributed deep learning frameworks, such as split learning, have recently been proposed to enable a group of participants to collaboratively train a deep neural network without sharing their raw data. Split learning in particular achieves this goal by dividing a neural network between a client and a server so that the client computes the initial set of layers, and the server computes the rest. However, this method introduces a unique attack vector for a malicious server attempting to steal the client\u27s private data: the server can direct the client model towards learning a task of its choice. With a concrete example already proposed, such training-hijacking attacks present a significant risk for the data privacy of split learning clients. In this paper, we propose SplitGuard, a method by which a split learning client can detect whether it is being targeted by a training-hijacking attack or not. We experimentally evaluate its effectiveness, and discuss in detail various points related to its use. We conclude that SplitGuard can effectively detect training-hijacking attacks while minimizing the amount of information recovered by the adversaries

Determination Of Electromyography-Based Coordinated Fatigue Levels In Agonist And Antagonist Muscles Of The Thigh During Squat Press Exercise

Background. Simultaneous tiredness of two or more muscles around a joint can be defined as coordinated fatigue (co-fatigue) and might occur between agonist and antagonist muscles, and vary according to the level of sporting activity levels or gender. Objectives. The aim of this study was to determine the levels of coordinated fatigue in agonist and antagonist muscles during squat-press exercise. Methods. Twenty athletes and twenty sedentary subjects participated in the study. Surface electromyography signals of the rectus femoris, vastus lateralis obliquus, biceps femoris and semitendinosus muscles were recorded at the squat press position for 15 seconds during isometric contraction. Measurements were repeated five times and a 2-minute rest period was allowed between repetitions. After erroneous EMG elimination, movement artefacts were removed by using a 20 Hz high-pass Butterworth filter. Then, as a well-recognized fatigue index, the median frequency (MF) of each filtered middle part of the EMG signal (5 to 10 s. of contraction) was calculated, given that it is known that the MF decreases during isometric contractions. Finally, each MF-based co-fatigue index was calculated by dividing the mean RF and VLO median frequencies by the mean ST and BF median frequencies. The cumulative co-fatigue values of “male vs. female” and “sedentary vs. athlete” comparisons were performed by using a two-sided Student t-test with a Bonferroni correction. Results. There was a statistically significant (Bonferroni corrected p-value < 0.05) difference between the mean female (1.57 +/- 0.53) and the mean male (1.23 +/- 0.17) co-fatigue values, while there was no statistically significant difference between the mean co-fatigue values of sedentary (1.51 +/- 0.52) and athlete (1.29 +/- 0.27) subjects. Conclusion. The offered co-fatigue indices might be useful for other sports, physiotherapy and related areas if sufficient scientific proof is accumulated.WoSScopu

Push and Pull Factors of Why Medical Students Want to Leave Türkiye: A Countrywide Multicenter Study

Phenomenon: Physician immigration from other countries is increasing as developed countries continue to be desirable destinations for physicians; however, the determinants of Turkish physicians’ migration decisions are still unclear. Despite its wide coverage in the media and among physicians in Türkiye, and being the subject of much debate, there is insufficient data to justify this attention. With this study, we aimed to investigate the tendency of senior medical students in Türkiye to pursue their professional careers abroad and its related factors. Approach: This cross-sectional study involved 9881 senior medical students from 39 different medical schools in Türkiye in 2022. Besides participants’ migration decision, we evaluated the push and pull factors related to working, social environment and lifestyle in Türkiye and abroad, medical school education inadequacy, and personal insufficiencies, as well as the socioeconomic variables that may affect the decision to migrate abroad. The analyses were carried out with a participation rate of at least 50%. Findings: Of the medical students, 70.7% had emigration intentions. Approximately 60% of those want to stay abroad permanently, and 61.5% of them took initiatives such as learning a foreign language abroad (54.5%) and taking relevant exams (18.9%). Those who wanted to work in the field of Research & Development were 1.37 (95% CI: 1.22–1.54) times more likely to emigrate. The push factor that was related to emigration intention was the “working conditions in the country” (OR: 1.89, 95% CI: 1.56–2.28) whereas the “social environment/lifestyle abroad” was the mere pull factor for the tendency of emigration (OR: 1.73, 95% CI: 1.45–2.06). In addition, the quality problem in medical schools also had a significant impact on students’ decisions (OR: 2.20, 95% CI: 1.83–2.65). Insights: Although the percentage of those who want to emigrate “definitely” was at the same level as in the other developing countries, the tendency to migrate “permanently” was higher in Türkiye. Improving working conditions in the country and increasing the quality of medical faculties seem vital in preventing the migration of physicians