The bread and the cross: an empirical analysis of religious discrimination in the egyptian labor market

Do Christians face discrimination in the Egyptian labor market? In the last few years, religious discrimination in the Egyptian labor market has been an ongoing debate between the Egyptian government, Christian activists, and international observers. Yet, no systemic empirical study of the issue was provided to enrich the debate with concrete objective evidence. As a result, this paper aims at filling this gap by empirically examining religious discrimination in wages, receipt of non-pecuniary benefits, working conditions, and access to different tracks of employment. Using recent data from the Egyptian Labor Market Panel Survey (ELMPS 2012), this study employs a set of econometric techniques including OLS regression analysis, propensity score matching, Oaxaca-Blinder decomposition, and probit models to determine the forms and extent of religious discrimination in the labor market. Our findings suggest that Christians do not face discrimination in wages, receipt of job’s non-pecuniary benefits, and working conditions. However, Christians have a disadvantage in access to wage employment in general, and government employment in particular, proposing religious discrimination as a possible explanation. These results enlighten the debate by defining the areas where discrimination is taking place and policies are needed

How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?

Federated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device. However, while data never leaves users' devices, privacy still cannot be guaranteed since significant computations on users' training data are shared in the form of trained local models. These local models have recently been shown to pose a substantial privacy threat through different privacy attacks such as model inversion attacks. As a remedy, Secure Aggregation (SA) has been developed as a framework to preserve privacy in FL, by guaranteeing the server can only learn the global aggregated model update but not the individual model updates. While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server. In this work, we perform a first analysis of the formal privacy guarantees for FL with SA. Specifically, we use Mutual Information (MI) as a quantification metric and derive upper bounds on how much information about each user's dataset can leak through the aggregated model update. When using the FedSGD aggregation algorithm, our theoretical bounds show that the amount of privacy leakage reduces linearly with the number of users participating in FL with SA. To validate our theoretical bounds, we use an MI Neural Estimator to empirically evaluate the privacy leakage under different FL setups on both the MNIST and CIFAR10 datasets. Our experiments verify our theoretical bounds for FedSGD, which show a reduction in privacy leakage as the number of users and local batch size grow, and an increase in privacy leakage with the number of training rounds.Comment: Accepted to appear in Proceedings on Privacy Enhancing Technologies (PoPETs) 202

Comparing the success rate of external dacryocystorhinostomy with anterior flap versus flap excision in managing chronic dacryocystitis

Background: Nasolacrimal duct obstruction (NLDO) is characterized by epiphora and recurrent episodes of acute dacryocystitis. Despite the temporary effect of antibiotics in the acute phase, it is primarily managed by dacryocystorhinostomy (DCR). There is a new modification of external DCR that is performed without either anterior or posterior flaps. This study aimed to compare the outcomes of flapless and single-flap external DCR in adult patients with chronic symptomatic dacryocystitis secondary to NLDO. Methods: In this retrospective, non-randomized, interventional, comparative study of patients with chronic dacryocystitis secondary to primary acquired NLDO, we compared the surgical outcomes and complication rates of flapless external DCR to those of external DCR with only anterior flap suturing. We excluded patients who declined participation and those with soft stops, nasal problems, lid margin abnormalities, lid malposition or laxity, previous lacrimal surgery, lacrimal fistula, trauma involving the lacrimal drainage system, lack of adequate follow-up, or severe septal deviation or turbinate hypertrophy. Anatomical and functional success rates were determined at the last follow-up visit and were compared. Postoperative complications were recorded and compared between groups. Results: We included 53 patients with a male-to-female ratio of 16 (30.2%) to 37 (69.8%); 25 eyes underwent flapless DCR (group 1) and 28 eyes underwent anterior flap suturing DCR (group 2). The two groups had comparable demographic characteristics (all P > 0.05). Furthermore, anatomical (92.0% in group 1 and 92.9% in group 2) and functional (84.0% in group 1 and 92.9% in group 2) success rates at final follow-up were comparable between groups (both P > 0.05). At the one-month postoperative examination, premature tube extrusion was more often reported in group 1 (12.0%) compared to group 2 (7.1%). At the two-month follow-up examination, tube extrusion was noted in 4.0% in group 1 and 0.0% in group 2, yet the difference failed to attain statistical significance (P > 0.05). Conclusions: We found that neither surgical method was superior in terms of anatomical or functional success rate at a maximum of one year after external DCR. Flapless DCR is a simple, effective, and reproducible alternative to the single anterior flap suturing technique for managing NLDO in adults with chronic dacryocystitis. However, further randomized clinical trials with larger sample sizes and longer follow-up periods are recommended before generalization can be justified

The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning

Secure aggregation promises a heightened level of privacy in federated learning, maintaining that a server only has access to a decrypted aggregate update. Within this setting, linear layer leakage methods are the only data reconstruction attacks able to scale and achieve a high leakage rate regardless of the number of clients or batch size. This is done through increasing the size of an injected fully-connected (FC) layer. However, this results in a resource overhead which grows larger with an increasing number of clients. We show that this resource overhead is caused by an incorrect perspective in all prior work that treats an attack on an aggregate update in the same way as an individual update with a larger batch size. Instead, by attacking the update from the perspective that aggregation is combining multiple individual updates, this allows the application of sparsity to alleviate resource overhead. We show that the use of sparsity can decrease the model size overhead by over 327$\times$ and the computation time by 3.34$\times$ compared to SOTA while maintaining equivalent total leakage rate, 77% even with $1000$ clients in aggregation.Comment: Accepted to CVPR 202

LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation

Federated learning was introduced to enable machine learning over large decentralized datasets while promising privacy by eliminating the need for data sharing. Despite this, prior work has shown that shared gradients often contain private information and attackers can gain knowledge either through malicious modification of the architecture and parameters or by using optimization to approximate user data from the shared gradients. However, prior data reconstruction attacks have been limited in setting and scale, as most works target FedSGD and limit the attack to single-client gradients. Many of these attacks fail in the more practical setting of FedAVG or if updates are aggregated together using secure aggregation. Data reconstruction becomes significantly more difficult, resulting in limited attack scale and/or decreased reconstruction quality. When both FedAVG and secure aggregation are used, there is no current method that is able to attack multiple clients concurrently in a federated learning setting. In this work we introduce LOKI, an attack that overcomes previous limitations and also breaks the anonymity of aggregation as the leaked data is identifiable and directly tied back to the clients they come from. Our design sends clients customized convolutional parameters, and the weight gradients of data points between clients remain separate even through aggregation. With FedAVG and aggregation across 100 clients, prior work can leak less than 1% of images on MNIST, CIFAR-100, and Tiny ImageNet. Using only a single training round, LOKI is able to leak 76-86% of all data samples.Comment: To appear in the IEEE Symposium on Security & Privacy (S&P) 202

Effect of Er,Cr:YSGG on Remineralization Using CPP - ACPF (MI - Paste Plus) after Enamel Erosion Caused by Carbonated Soft Drink in Primary Teeth: In-Vitro Study

BACKGROUND: Erosion is a widespread phenomenon with higher predilection in primary dentition. AIM: The aim of the present study is to assess the remineralising effect of Er,Cr:YSGG laser application combined with CPP-ACPF after erosive demineralisation by Coca-Cola in primary teeth. METHODS: Fifty teeth (n = 10) were divided into; Group I: Artificial saliva, (Saliva natural, Medac, UK), Group II: CPP-ACPF (MI Paste Plus, GC Corp, USA), Group III: Er,Cr:YSGG (Waterlase iPlus, USA), Group IV: CPP-ACPF + Er,Cr:YSGG, Group V: Er,Cr:YSGG + CPP-ACPF. Teeth were immersed in Coca-Cola for 10 min, 5 times/day for 5 days. DIAGNOdent (DD) measurements were taken before and after the experiment. RESULTS: There was a significant increase in DD readings after erosive-treatment cycles in all test groups. The highest reading was in samples immersed in artificial saliva, and the lowest was in those subjected to combined CPP-ACPF and Er,Cr:YSGG laser application, regardless of the sequence used. There was no significant difference between samples immersed in artificial saliva, and after CPP-ACPF application. Similarly, there was no significant difference between samples treated by combined treatment of CPP-ACPF and Er,Cr:YSGG application. However, there was a significant difference between samples immersed in artificial saliva or treated with CPP-ACPF application and those subjected to combined treatment CPP-ACPF along with Er,Cr:YSGG. CONCLUSION: Combining Er,Cr:YSGG laser and CPP-ACPF paste significantly increased enamel remineralisation, regardless of the sequence implemented. Saliva naturally and CPP-ACPF application had a comparable effect on remineralisation