Photoluminescent soft materials have been widely applied in sensing, display devices, and organic light-emitting diodes, [1] and also have received great attention toward security protection applications in information storage, date recording and encryption. [2] In particular, luminescent hydrogel-based 3D codes prepared in environmentally friendly process could not only increase the information density per unit area but also be employed as wearable or biological anti-counterfeiting materials. [3] On the other hand, the information recorded directly in these materials is usually visible under either ambient or UV light, which would hamper their practical applications in confidential information protection because these anti-counterfeiting labels could be easily mimicked. [4] In this context, smart luminescent materials that can perceive the surrounding stimuli and respond to them should be ideal for confidential information protection. [5] Under external stimuli, the luminescent outputs of these materials can be precisely modulated, preventing the information from being stolen or mimicked. [6,7] Stimulus-responsive luminescent materials that rely on constant addition of chemicals have been developed for information encryption and decryption. [8] Since these methods require invasive stimuli, it may be difficult for consumers without professional chemistry knowledge to handle the encoded information by adding chemicals. Therefore, it is highly desirable to develop alternative switchable luminescent materials with confidential encryption property capable of being easily operated in a noninvasive manner, where the security codes are initially invisible and become visible under specific external stimuli. In this way, reversible information encryption and decryption could be achieved. Light irradiation is an appealing external stimulus because it provides clean, spatiotemporal, and noninvasive control on the operation with high precision, [9] showing greater convenience in activating or erasing the code information as compared to other chemical stimuli. [10] In terms of emitting sources, lanthanide complexes are excellent emitting centers because of their intriguing optical properties, such as narrow emission bands, large Stokes shift, high luminescent efficiency, and long luminescence lifetime. [7,11] To the best of our knowledge, however, achieving Conventional luminescent information is usually visible under either ambient or UV light, hampering their potential application in smart confidential information protection. In order to address this challenge, herein, light-triggered luminescence ON-OFF switchable hybrid hydrogels are successfully constructed through in situ copolymerization of acrylamide, lanthanide complex, and diarylethene photochromic unit. The open-close behavior of the diarylethene ring in the polymer could be controlled by UV and visible light irradiation, where the close form of the ring features fluorescence resonance energy transfer with the lanthanide complex. The hydrogel-based block...

The relational database has become one of the mainstream tools for data storage and management. However, there are two main types of threats to relational databases: external attacks and internal tampering threats. In this paper, we focus on the internal tampering threats and propose a tamper-proof detection middleware named TDRB to provide efficient tamper-proof detection for relational databases. Within the TDRB middleware framework, raw data is still stored and queried from the relational database, while the hash digest of the critical data in the relational database is synchronously migrated to the blockchain for tamper detection. Based on this method, we leverage blockchain's immutability to detect data tamper and maintain the advanced features of relational databases to better support ease of data persistence, complex queries, and large storage capacity. We also propose a performance improvement mechanism that involves connecting the blockchain and relational database to improve throughput and mitigate performance impact. A series of experiments indicate that the TDRB middleware can accurately detect the tampering information during arbitrary tampering with the relational database and the cache database. Compare with the baseline, with the increase of cache hit rate, the TDRB middleware query speed increased by 93.1%, update speed increased by 16.3%, delete speed increased by 16.1%, and join operation average speed increased by 95.2%. Given its generality, the TDRB middleware can be flexibly and conveniently integrated into third-party platforms.

Internet of Things (IoT) technology allows us to measure, compute, and decide about the physical world around us in a quantitative and intelligent way. It makes all kinds of intelligent IoT devices popular. We are continually perceived and recorded by intelligent IoT devices, especially vision devices such as cameras and mobile phones. However, a series of security issues have arisen in recent years. Sensitive data leakage is the most typical and harmful one. Whether we are just browsing files unintentionally in sight of high-definition (HD) security cameras, or internal ghosts are using mobile phones to photograph secret files, it causes sensitive data to be captured by intelligent IoT vision devices, resulting in irreparable damage. Although the risk of sensitive data diffusion can be reduced by optical character recognition (OCR)-based packet filtering, it is difficult to use it with sensitive data presented in table form. This is because table images captured by the intelligent IoT vision device face issues of perspective transformation, and interferences of circular stamps and irregular handwritten signatures. Therefore, a table-recognition algorithm based on a directional connected chain is proposed in this paper to solve the problem of identifying sensitive table data captured by intelligent IoT vision devices. First, a Directional Connected Chain (DCC) search algorithm is proposed for line detection. Then, valid line mergence and invalid line removal is performed for the searched DCCs to detect the table frame, to filter the irregular interferences. Finally, an inverse perspective transformation algorithm is used to restore the table after perspective transformation. Experiments show that our proposed algorithm can achieve accuracy of at least 92%, and filter stamp interference completely.