Mobile edge computing (MEC) is a promising approach to reduce the network traffic load and alleviate the back-haul congestion by pushing computation down to the network edge (e.g., base stations) that are close to the origin of data. However, when many mobile devices (MDs) offload tasks to a base station (BS) in a dynamic and stochastic environment (e.g., with time-varying wireless channels and uncertain task models), it is often challenging for MDs to make offloading decisions in decentralized manner. In this work, we consider a collaborative MEC scenario, where an MD can offload its task to the associated BS or to other BSs through the associated BS. In such a scenario, we study the joint computation offloading and resource allocation problem, aiming at minimizing the expected long-term delay, taking the energy consumption constraint into consideration. The problem is challenging due to time-varying system and distributed decisions. To solve the problem in an online and decentralized manner, we propose a deep reinforcement learning (DRL) based distributed online algorithm. By incorporating the double deep Q network and dueling deep Q network technique, the proposed algorithm can improve the performance of the whole system significantly. Simulation results show that the proposed DRL-based algorithm outperforms baseline methods and can reduce the average delay of tasks by 76.4%-91.2%.

The payment of most popular cryptocurrencies such as Bitcoin or Ethereum is based on blockchain addresses, which are usually two random strings that are not easily memorialized or managed by human beings. On the other hand, traditional payment is usually based on human-readable identifiers (HRI). We believe that the decentralized identifiers such as Ethereum naming service (ENS) combined with the users’ existing various online identities such as mobile phone numbers, social media accounts, etc, will constitute the main HRI layer for the payment network of Web 3.0, and most payments in the Web 3.0 world will happen between the HRI accounts.This work presents a cross-platform private payment scheme for HRIs, that allows a user to manage multiple HRIs with one single secret key. Our payment scheme guarantees the confidentiality of the payment amounts and the anonymity of users involved in the payment. We also provide a mechanism for verifiable HRI, which paves the way for many interesting applications such as electronic invoicing, regulation-compliant DeFi, etc.

In a world where traditional notions of privacy are increasingly challenged by the myriad companies that collect and analyze our data, it is important that decision-making entities are held accountable for unfair treatments arising from irresponsible data usage. Unfortunately, a lack of appropriate methodologies and tools means that even identifying unfair or discriminatory effects can be a challenge in practice. We introduce the unwarranted associations (UA) framework, a principled methodology for the discovery of unfair, discriminatory, or offensive user treatment in data-driven applications. The UA framework unifies and rationalizes a number of prior attempts at formalizing algorithmic fairness. It uniquely combines multiple investigative primitives and fairness metrics with broad applicability, granular exploration of unfair treatment in user subgroups, and incorporation of natural notions of utility that may account for observed disparities. We instantiate the UA framework in FairTest, the first comprehensive tool that helps developers check data-driven applications for unfair user treatment. It enables scalable and statistically rigorous investigation of associations between application outcomes (such as prices or premiums) and sensitive user attributes (such as race or gender). Furthermore, FairTest provides debugging capabilities that let programmers rule out potential confounders for observed unfair effects. We report on use of FairTest to investigate and in some cases address disparate impact, offensive labeling, and uneven rates of algorithmic error in four data-driven applications. As examples, our results reveal subtle biases against older populations in the distribution of error in a predictive health application and offensive racial labeling in an image tagger.

Trusted hardware systems, such as Intel's new SGX instruction set architecture extension, aim to provide strong confidentiality and integrity assurances for applications. Recent work, however, raises serious concerns about the vulnerability of such systems to side-channel attacks. We propose, formalize, and explore a cryptographic primitive called a Sealed-Glass Proof (SGP) that models computation possible in an isolated execution environment with unbounded leakage, and thus in the face of arbitrary side-channels. A SGP specifically models the capabilities of trusted hardware that can attest to correct execution of a piece of code, but whose execution is transparent, meaning that an application's secrets and state are visible to other processes on the same host. Despite this strong threat model, we show that SGPs enable a range of practical applications. Our key observation is that SGPs permit safe verifiable computing in zero-knowledge, as data leakage results only in the prover learning her own secrets. Among other applications, we describe the implementation of an end-to-end bug bounty (or zero-day solicitation) platform that couples a SGX-based SGP with a smart contract. Our platform enables a marketplace that achieves fair exchange, protects against unfair bounty withdrawals, and resists denial-of-service attacks by dishonest sellers. We also consider a slight relaxation of the SGP model that permits black-box modules instantiating minimal, side-channel resistant primitives, yielding a still broader range of applications. Our work shows how trusted hardware systems such as SGX can support trustworthy applications even in the presence of side channels.

MOTIVATION: Due to the limited power of small-scale genome-wide association studies (GWAS), researchers tend to collaborate and establish a larger consortium in order to perform large-scale GWAS. Genome-wide association meta-analysis (GWAMA) is a statistical tool that aims to synthesize results from multiple independent studies to increase the statistical power and reduce false-positive findings of GWAS. However, it has been demonstrated that the aggregate data of individual studies are subject to inference attacks, hence privacy concerns arise when researchers share study data in GWAMA. RESULTS: In this article, we propose a secure quality control (SQC) protocol, which enables checking the quality of data in a privacy-preserving way without revealing sensitive information to a potential adversary. SQC employs state-of-the-art cryptographic and statistical techniques for privacy protection. We implement the solution in a meta-analysis pipeline with real data to demonstrate the efficiency and scalability on commodity machines. The distributed execution of SQC on a cluster of 128 cores for one million genetic variants takes less than one hour, which is a modest cost considering the 10-month time span usually observed for the completion of the QC procedure that includes timing of logistics. AVAILABILITY AND IMPLEMENTATION: SQC is implemented in Java and is publicly available at https://github.com/acs6610987/secureqc. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile communications and cloud computing technologies to provide feedback decision support, has been considered as a revolutionary approach to improving the quality of healthcare service while lowering the healthcare cost. Unfortunately, it also poses a serious risk on both clients' privacy and intellectual property of monitoring service providers, which could deter the wide adoption of mHealth technology. This paper is to address this important problem and design a cloud-assisted privacy preserving mobile health monitoring system to protect the privacy of the involved parties and their data. Moreover, the outsourcing decryption technique and a newly proposed key private proxy reencryption are adapted to shift the computational complexity of the involved parties to the cloud without compromising clients' privacy and service providers' intellectual property. Finally, our security and performance analysis demonstrates the effectiveness of our proposed design.

Since there always exists a possibility that some users' private keys are stolen or expired in practice, it is important for identity based encryption (IBE) system to provide a solution to revocation. The current most efficient revocable IBE system has a private key of size O(log ns) and update information of size O(r log(n/r)) where r is the number of revoked users. In this paper, we present a new revocable IBE system in which the private key only contains two group elements and the update information size is O(r). We show that the proposed constructions for the revocation mechanism are more efficient in terms of space cost and provide a generic methodology to transform a non-monotonic attribute based encryption into a revocable IBE. We also demonstrate how the proposed method can be employed to develop an efficient hierarchical revocable IBE system.

Attribute based proxy re-encryption scheme (ABPRE) is a new cryptographic primitive which extends the traditional proxy re-encryption (public key or identity based cryptosystem) to the attribute based counterpart, and thus empower users with delegating capability in the access control environment. Users, identified by attributes, could freely designate a proxy who can re-encrypt a ciphertext related with a certain access policy to another one with a different access policy. The proposed scheme is proved selective-structure chosen plaintext secure and master key secure without random oracles. Besides, we develop another kind of key delegating capability in our scheme and also discuss some related issues including a stronger security model and applications.

Ciphertext policy attribute based encryption (CPABE) allows a sender to distribute messages based on an access policy which can be expressed as a boolean function consisting of (OR, AND) gates between attributes. A receiver whose secret key is associated with those attributes could only decrypt a ciphertext successfully if and only if his attributes satisfy the ciphertext's access policy. Fine-grained access control, a new concept mentioned by GPSW in CCS'06 can realize a more delicate access policy which could be represented as an access tree with threshold gates connecting attributes.