Separable Reversible Data Hiding in Encrypted Images Based on Two-Dimensional Histogram Modification

An efficient method of completely separable reversible data hiding in encrypted images is proposed. The cover image is first partitioned into nonoverlapping blocks and specific encryption is applied to obtain the encrypted image. Then, image difference in the encrypted domain can be calculated based on the homomorphic property of the cryptosystem. The data hider, who does not know the original image content, may reversibly embed secret data into image difference based on two-dimensional difference histogram modification. Data extraction is completely separable from image decryption; that is, data extraction can be done either in the encrypted domain or in the decrypted domain, so that it can be applied to different application scenarios. In addition, data extraction and image recovery are free of any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme

Resilient neural network training for accelerators with computing errors

—With the advancements of neural networks, customized accelerators are increasingly adopted in massive AI applications. To gain higher energy efficiency or performance, many hardware design optimizations such as near-threshold logic or overclocking can be utilized. In these cases, computing errors may happen and the computing errors are difficult to be captured by conventional training on general purposed processors (GPPs). Applying the offline trained neural network models to the accelerators with errors directly may lead to considerable prediction accuracy loss. To address this problem, we explore the resilience of neural network models and relax the accelerator design constraints to enable aggressive design options. First of all, we propose to train the neural network models using the accelerators’ forward computing results such that the models can learn both the data and the computing errors. In addition, we observe that some of the neural network layers are more sensitive to the computing errors. With this observation, we schedule the most sensitive layer to the attached GPP to reduce the negative influence of the computing errors. According to the experiments, the neural network models obtained from the proposed training outperform the original models significantly when the CNN accelerators are affected by computing errors

Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions

The Right to be Forgotten (RTBF) was first established as the result of the ruling of Google Spain SL, Google Inc. v AEPD, Mario Costeja Gonz\'alez, and was later included as the Right to Erasure under the General Data Protection Regulation (GDPR) of European Union to allow individuals the right to request personal data be deleted by organizations. Specifically for search engines, individuals can send requests to organizations to exclude their information from the query results. With the recent development of Large Language Models (LLMs) and their use in chatbots, LLM-enabled software systems have become popular. But they are not excluded from the RTBF. Compared with the indexing approach used by search engines, LLMs store, and process information in a completely different way. This poses new challenges for compliance with the RTBF. In this paper, we explore these challenges and provide our insights on how to implement technical solutions for the RTBF, including the use of machine unlearning, model editing, and prompting engineering

SeePrivacy: Automated Contextual Privacy Policy Generation for Mobile Applications

Privacy policies have become the most critical approach to safeguarding individuals' privacy and digital security. To enhance their presentation and readability, researchers propose the concept of contextual privacy policies (CPPs), aiming to fragment policies into shorter snippets and display them only in corresponding contexts. In this paper, we propose a novel multi-modal framework, namely SeePrivacy, designed to automatically generate contextual privacy policies for mobile apps. Our method synergistically combines mobile GUI understanding and privacy policy document analysis, yielding an impressive overall 83.6% coverage rate for privacy-related context detection and an accuracy of 0.92 in extracting corresponding policy segments. Remarkably, 96% of the retrieved policy segments can be correctly matched with their contexts. The user study shows SeePrivacy demonstrates excellent functionality and usability (4.5/5). Specifically, participants exhibit a greater willingness to read CPPs (4.1/5) compared to original privacy policies (2/5). Our solution effectively assists users in comprehending privacy notices, and this research establishes a solid foundation for further advancements and exploration

Lipid and carbohydrate modifications of α-galactosylcer-amide differently influence mouse and human type I natural killer T cell activation

The ability of different glycosphingolipids (GSLs) to activate type I natural killer T cells (NKT cells) has been known for 2 decades. The possible therapeutic use of these GSLs has been studied in many ways; however, studies are needed in which the efficacy of promising GSLs is compared under identical conditions. Here, we compare five unique GSLs structurally derived from alpha-galactosylceramide. We employed biophysical and biological assays, as well as x-ray crystallography to study the impact of the chemical modifications of the antigen on type I NKT cell activation. Although all glycolipids are bound by the T cell receptor of type I NKT cells in real time binding assays with high affinity, only a few activate type INKT cells in in vivo or in vitro experiments. The differences in biological responses are likely a result of different pharmacokinetic properties of each lipid, which carry modifications at different parts of the molecule. Our results indicate a need to perform a variety of assays to ascertain the therapeutic potential of type I NKT cell GSL activators

Use of a Generalized Additive Model to Investigate Key Abiotic Factors Affecting Microcystin Cellular Quotas in Heavy Bloom Areas of Lake Taihu

Lake Taihu is the third largest freshwater lake in China and is suffering from serious cyanobacterial blooms with the associated drinking water contamination by microcystin (MC) for millions of citizens. So far, most studies on MCs have been limited to two small bays, while systematic research on the whole lake is lacking. To explain the variations in MC concentrations during cyanobacterial bloom, a large-scale survey at 30 sites across the lake was conducted monthly in 2008. The health risks of MC exposure were high, especially in the northern area. Both Microcystis abundance and MC cellular quotas presented positive correlations with MC concentration in the bloom seasons, suggesting that the toxic risks during Microcystis proliferations were affected by variations in both Microcystis density and MC production per Microcystis cell. Use of a powerful predictive modeling tool named generalized additive model (GAM) helped visualize significant effects of abiotic factors related to carbon fixation and proliferation of Microcystis (conductivity, dissolved inorganic carbon (DIC), water temperature and pH) on MC cellular quotas from recruitment period of Microcystis to the bloom seasons, suggesting the possible use of these factors, in addition to Microcystis abundance, as warning signs to predict toxic events in the future. The interesting relationship between macrophytes and MC cellular quotas of Microcystis (i.e., high MC cellular quotas in the presence of macrophytes) needs further investigation