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

Relationship between disease severity and D-dimer levels measured with two different methods in pulmonary embolism patients

<p>Abstract</p> <p>Pulmonary embolism (PE) is diagnosed with increasing frequency nowadays due to advances in the diagnostic methods and the increased awareness of the disease. There is a tendency to use non invasive diagnostic methods for all diseases. D-dimer is a fibrin degradation product. We aimed to detect the relationship between disease severity and the D-dimer levels measured with two different methods. We compared D-dimer levels in cases of massive vs. non-massive PE. A total of 89 patients who were diagnosed between 2006 and 2008 were included in the study. Group 1 included patients whose D-dimer levels were measured with the immunoturbidimetric polyclonal antibody method (D-dimerPLUS^®), while Group 2 patients made use of the immunoturbidimetric monoclonal antibody method (InnovanceD-DIMER^®). In each group, the D-dimer levels of those with massive and non-massive PE were compared, using the Mann Whitney U test. The mean age of Group 1 (25 F/26 M) was 56.0 ± 17.9 years, and that of Group 2 (22 F/16 M) was 52.9 ± 17.9 years. There was no statistical difference in gender and mean age between the two groups (p > 0.05). In Group 1, the mean D-dimer level of massive cases (n = 7) was 1444.9 ± 657.9 μg/L and that of nonmassive PE (n = 34) was 1304.7 ± 350.5 μg/L (p > 0.05). In Group 2, the mean D-dimer level of massive cases (n = 6) was 9.7 ± 2.2 mg/L and that of non-massive PE (n = 32) was 5.9 ± 1.3 mg/L (p < 0.05). The mean D-dimer levels of massive cases as measured with the immunoturbidimetric monoclonal antibody method were significantly higher. Pulmonary embolism patients whose D-dimer levels are higher (especially higher than 6.6 mg/L) should be considered as possibly having massive embolism. Diagnostic procedures and management can be planned according to this finding.</p

Acceleration of in vitro dissolution studies of sustained release dosage form of theophylline and in vitro-in vivo evaluations in terms of correlations

WOS: 000300772200008PubMed ID: 21739190The aim of the study was to accelerate the dissolution of the sustained release dosage forms using both elevated temperature and high rpm rates. Teokap (R) SR 200 mg pellets were tested by in vitro sustained and accelerated dissolution studies using USP XXIII rotating paddle method. Sustained dissolution studies were carried out for 12 h in phosphate buffer at 37 +/- 0.5 degrees C and 50 rpm. Accelerated dissolution studies were performed for 48 min in distilled water at 90 +/- 1 degrees C and 250 rpm. The results obtained from accelerated and sustained dissolution studies were correlated using both linear and linear kinetic correlation methods by a computer program. While r(2) and maximum error between calculated and observed drug release rates were found to be 0.9129 and 15.9%, respectively, in linear correlation method, these values were observed as 0.9938 and 3.6%, respectively, in linear kinetic correlation method. In vivo plasma concentration data were obtained from six New Zealand rabbits after administration of a single dose of Teokap (R) SR 200 mg pellet. Then, the results of in vivo study were evaluated with in vitro accelerated and sustained dissolution results by applying them to in vitro-in vivo linear correlations. As a result of these correlations, it was shown that the in vitro correlation plots were very similar to the plot which was obtained by the in vivo study (f(2) = 73.81-77.11). This study suggested a way to prevent the loss of time for routine dissolution studies of sustained release preparations for quality control procedures using in vitro accelerated dissolution tests. The storage and quarantine periods of the product in process and process controls in the manufactories could be shortened by this method. Calculation of the in vivo performance of sustained release dosage forms using the results of the accelerated dissolution studies may be counted as another advantage of the method

Ghrelin, leptin, adiponectin, and resistin levels in sleep apnea syndrome: Role of obesity

Aim: The aim of this study was to investigate the relationship among plasma leptin, ghrelin, adiponectin, resistin levels, and obstructive sleep apnea syndrome (OSAS). Methods: Fifty-five consecutive newly diagnosed OSAS patients and 15 age-matched nonapneic controls were enrolled in this study. After sleep study between 8:00 AM and 9:00 AM on the morning, venous blood was obtained in the fasting state to measure ghrelin and adipokines. Results: Serum ghrelin levels of OSAS group were significantly (P < 0.05) higher than those of the control group. No significant difference was noted in the levels of leptin, adiponectin, and resistin in OSAS group when compared to controls. There was a significant positive correlation between ghrelin and apnea-hypopnea index (AHI) (r = 0.237, P < 0.05) or the Epworth sleepiness scale (ESS) (r = 0.28, P < 0.05). There was also a significant positive correlation between leptin and body mass index (r = 0.592, P < 0.0001). No significant correlation was observed between leptin, adiponectin, resistin, and any polysomnographic parameters. Conclusion : Our findings demonstrated that serum ghrelin levels were higher in OSAS patients than those of control group and correlated with AHI and ESS. Further studies are needed to clarify the complex relation among OSAS, obesity, adipokines, and ghrelin