Related papers: Serializable Snapshot Isolation in PostgreSQL
Sensor systems are extremely popular today and vulnerable to sensor data attacks. Due to possible devastating consequences, counteracting sensor data attacks is an extremely important topic, which has not seen sufficient study. This paper…
Dataset distillation methods have achieved remarkable success in distilling a large dataset into a small set of representative samples. However, they are not designed to produce a distilled dataset that can be effectively used for…
Current blockchains do not provide any security guarantees to the smart contracts and their users as far as the content of the transactions is concerned. In the spirit of decentralization and censorship resistance, they follow the paradigm…
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most of methods mainly focus on the instance level information…
Modern applications, such as social networking systems and e-commerce platforms are centered around using large-scale databases for storing and retrieving data. Accesses to the database are typically enclosed in transactions that allow…
Supervised fine-tuning (SFT) is a pivotal approach to adapting large language models (LLMs) for downstream tasks; however, performance often suffers from the ``seesaw phenomenon'', where indiscriminate parameter updates yield progress on…
Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for decades. The primary family of SSL algorithms, known as pseudo-labeling, involves assigning pseudo-labels to confident unlabeled instances and…
Hyperledger Fabric is a distributed operating system for permissioned blockchains hosted by the Linux Foundation. It is the first truly extensible blockchain system for running distributed applications at enterprise grade scale. To achieve…
Modern FaaS systems perform well in the case of repeat executions when function working sets stay small. However, these platforms are less effective when applied to more complex, large-scale and dynamic workloads. In this paper, we…
Dataset distillation is an advanced technique aimed at compressing datasets into significantly smaller counterparts, while preserving formidable training performance. Significant efforts have been devoted to promote evaluation accuracy…
Concurrency control (CC) algorithms must trade off strictness for performance. Serializable CC schemes generally pay higher cost to prevent anomalies, both in runtime overhead and in efforts wasted by aborting transactions. We propose the…
The hardware transactional memory (HTM) implementations in commercially available processors are significantly hindered by their tight capacity constraints. In practice, this renders current HTMs unsuitable to many real-world workloads of…
Various logic-locking schemes have been proposed to protect hardware from intellectual property piracy and malicious design modifications. Since traditional locking techniques are applied on the gate-level netlist after logic synthesis,…
Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches.…
The centralized PKI is not a suitable solution to provide identities in large-scale IoT systems. The main problem is the high cost of managing X.509 certificates throughout their lifecycle, from installation to regular updates and…
Provenance for transactional updates is critical for many applications such as auditing and debugging of transactions. Recently, we have introduced MV-semirings, an extension of the semiring provenance model that supports updates and…
The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often resulting in models with complex training strategies to…
Plenty of in-process vulnerabilities are blamed on various out of bound memory accesses. Previous prevention methods are mainly based on software checking associated with performance overhead, while traditional hardware protection…
With the continuous increase in the number of software-based attacks, there has been a growing effort towards isolating sensitive data and trusted software components from untrusted third-party components. A hardware-assisted intra-process…
This work considers computationally efficient privacy-preserving data release. We study the task of analyzing a database containing sensitive information about individual participants. Given a set of statistical queries on the data, we want…