Bilinear maps are used as an essential cryptographic building block in many of the advanced encryption algorithms today, such as searchable encryption, identity-based encryption, group signatures, etc. Numerous data and application privacy techniques make use of such primitives. However, the performance overhead of bilinear map encryption, and in particular that of the \em pairing operation, which is the predominant operation on bilinear maps, is still quite high. In this paper, we investigate in-depth the sequence of steps required to compute bilinear map pairings, and we identify the performance footprint of each step. We devise an implementation based on FPGA which reduces the overhead of bilinear pairings, in terms of both execution time and resource utilization (i.e., lookup tables and flip-flop units required). Our extensive performance evaluation shows that the proposed approach significantly outperforms benchmarks, and represents an important step towards the wide-scale deployment of bilinear map-based encryption protocol for large-scale applications.

Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains, including data augmentation and synthetic data generation. This work explores the use of LLMs to generate rich textual descriptions for motion sequences, encompassing both actions and walking patterns. We leverage the expressive power of LLMs to align motion representations with high-level linguistic cues, addressing two distinct tasks: action recognition and retrieval of walking sequences based on appearance attributes. For action recognition, we employ LLMs to generate textual descriptions of actions in the BABEL-60 dataset, facilitating the alignment of motion sequences with linguistic representations. In the domain of gait analysis, we investigate the impact of appearance attributes on walking patterns by generating textual descriptions of motion sequences from the DenseGait dataset using LLMs. These descriptions capture subtle variations in walking styles influenced by factors such as clothing choices and footwear. Our approach demonstrates the potential of LLMs in aug-menting structured motion attributes and aligning multimodal representations. The findings contribute to the advancement of comprehensive motion understanding and open up new av-enues for leveraging LLMs in multimodal alignment and data augmentation for motion analysis. We make the code publicly available at https://github.com/Radu1999/WalkAndText

Most modern equipment, either industrial, home use or special purpose, deals with specific physical parameters measurement. Out of these, temperature is one of the most meaningful and commonly measured parameters. Whether developing a custom-built measurement system or just a signal conditioning circuit for off-the-shelf measurement systems, the accuracy of the measured values is of paramount importance. Both general purpose and specialized temperature measurement acquisition systems have a specified accuracy on component tolerances and parameter drift. Such systems are specified with an initial accuracy and they should remain within specifications during operation in their nominal conditions (temperature range, humidity, altitude). Despite efforts in the design stage, many errors are later generated by the temperature drift of the components. The current papers presents a non-statistical simulator solution based on mathematical models for thermo-resistive (NTC, RTD) based digital temperature measurement systems. While designing the mathematical models, we also debate the influence of specific parameters on the total measurement error.

In agriculture, if the harvest isn't consumed in short term, proper storage conditions must be assured, usually by monitoring and controlling parameters like temperature and humidity. Our paper presents SiloSense, a novel ZigBee-based architecture for monitoring storage conditions of grain silos with the purpose of guarding them against spoilage and infections. It describes a scalable solution for data acquisition using a network of custom-made boards that are continuously capturing sensor samples and send it for analysis into the cloud. It discusses the engineering problems that were encountered and how we overcame them.

Private Information Retrieval (PIR) protocols allow users to search for data items stored at an untrusted server, without disclosing to the server the search attributes. Several computational PIR protocols provide cryptographic-strength guarantees for the privacy of users, building upon well-known hard mathematical problems, such as factorisation of large integers. Unfortunately, the computational-intensive nature of these solutions results in significant performance overhead, preventing their adoption in practice. In this paper, we employ graphical processing units (GPUs) to speed up the cryptographic operations required by PIR. We identify the challenges that arise when using GPUs for PIR and we propose solutions to address them. To the best of our knowledge, this is the first work to use GPUs for efficient private information retrieval, and an important first step towards GPU-based acceleration of a broader range of secure data operations. Our experimental evaluation shows that GPUs improve performance by more than an order of magnitude.

Monitoring location updates from mobile users has important applications in several areas, ranging from public safety and national security to social networks and advertising. However, sensitive information can be derived from movement patterns, so protecting the privacy of mobile users is a major concern. Users may only be willing to disclose their locations when some condition is met, for instance in proximity of a disaster area, or when an event of interest occurs nearby. Currently, such functionality is achieved using searchable encryption. Such cryptographic primitives provide provable guarantees for privacy, and allow decryption only when the location satisfies some predicate. Nevertheless, they rely on expensive pairing-based cryptography (PBC), and direct application to the domain of location updates leads to impractical solutions.

Provenance workflows capture the data movement and the operations changing the data in complex applications such as scientific computations, document management in large organizations, content generation in social media, etc. Provenance is essential to understand the processes and operations that data undergo, and many research efforts focused on modeling, capturing and analyzing provenance information. Sharing provenance brings numerous benefits, but may also disclose sensitive information, such as secret processes of synthesizing chemical substances, confidential business practices and private details about social media participants' lives. In this paper, we study privacy-preserving provenance workflow publication using differential privacy. We adapt techniques designed for sanitization of multi-dimensional spatial data to the problem of provenance workflows. Experimental results show that such an approach is feasible to protect provenance workflows, while at the same time retaining a significant amount of utility for queries. In addition, we identify influential factors and trade-offs that emerge when sanitizing provenance workflows.

We analyze in-depth five smoking cessation apps on Android OS, examining how they teach users to quit smoking and what they learn from users. Apps advise would-be ex-smokers how to perceive the world, how to deal with their emotions, and how to act on their bodies and environment. Still, they learn little from their users, and even less from the scientific literature on smoking cessation. We discuss the potential for improved customization of advice to users’ profiles and we propose a simple inventory of online scientific resources as a starting point for developers looking to create better apps.

We analyze current approaches in motivating students to pursue accessibility, with a focus on blind users, by examining scientific reports of courses in the computer science and engineering curriculum. We identify three main motivational resorts: a ‘web of arguments’, referring to issues of morality, legality, and interest; the practice of mainstreaming, which normalizes accessibility, and empathy. We argue that an aesthetic frame could contribute to a forceful, persistent motivation, and we propose an aesthetic motivational repertoire, on three dimensions: aesthetic value of technological tools, of engineers’ own work, and of their direct and indirect relationships with blind persons. We present arguments, practices, and online resources to support teachers that introduce accessibility for blind users to sighted students.

Latest generation mobile devices allow users to receive services tailored to their current locations. Location-based service providers perform spatial queries based on the user locations, but may also share them with various third parties. User whereabouts may disclose sensitive details about an individual's health status, political views or lifestyle choices, and therefore must be thoroughly protected. Private information retrieval (PIR) methods support blind execution of range and NN queries with cryptographic-strength security, but incur significant performance overhead. We employ graphical processing units (GPUs) to speed up the crypto operations required by PIR. We identify the challenges that arise when using GPUs for this purpose, and we propose solutions to address them. To the best of our knowledge, this is the first work to use GPUs for efficient private spatial information retrieval, and an important first step towards GPU-based acceleration of a broader range of secure spatial data operations.

Predicate evaluation on encrypted data is a challenge that modern cryptography is starting to address. The advantages of constructing logical primitives that are able to operate on encrypted data are numerous, such as allowing untrusted parties to take decisions without actually having access to the plaintext. Systems that offer these methods are grouped under the name of searchable encryption systems. One of the challenges that searchable encryption faces today is related to computational and bandwidth costs, because the mathematical operations involved are expensive. Recent algorithms such as Hidden Vector Encryption exhibit improved efficiency, but for large scale systems the optimizations are often not enough. Many problems that can be solved using searchable encryption are embarrassingly parallel. Using a prototype, we show that parallel solutions offer sufficient cost reduction so that large scale applications become feasible.

Monitoring user location updates has important applications in public safety, national security, etc. However, sensitive information can be derived from movement patterns, so user locations must be disclosed only when some condition is met, for instance in proximity of a disaster area. Searchable encryption techniques provide provable guarantees for privacy, and allow decryption only when the location satisfies some predicate. Nevertheless, they rely on expensive pairing-based cryptography, and direct application to location updates leads to impractical solutions. We propose an efficient technique that leads to significant gains in performance by reducing the amount of pairing operations. We also implement an optimization that reuses results to expensive mathematical operations. Experimental results show that the proposed techniques significantly improve performance compared to the baseline.