大数据安全与隐私的十大挑战

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"本文概述了大数据安全和隐私的十大挑战，强调了在大数据时代，由于数据的高速度、大量性和多样性，如大规模云基础设施、多样化数据源和格式、数据流处理以及高频率的跨云迁移，传统的安全机制已无法满足需求。文章旨在引起对加强大数据基础设施安全性的更多关注。" 在大数据保护的背景下，以下是一些关键的安全和隐私挑战的详细说明： 1. **数据量巨大（Volume）**：大数据的规模使得传统安全措施难以全面覆盖，增加了数据泄露的风险。这需要新的方法来管理、保护和监控海量数据。 2. **数据速度（Velocity）**：数据的实时生成和快速流动要求安全解决方案能够实时响应，防止恶意活动和即时威胁。 3. **数据多样性（Variety）**：多样化的数据源和格式增加了复杂性，使得数据分类、保护策略制定和合规性管理成为难题。 4. **云基础设施的安全**：大规模云环境中的数据存储和处理带来了新的安全挑战，包括云服务提供商的可信度、数据隔离、以及跨境数据传输的法规遵循。 5. **数据来源的多样性**：来自不同来源的数据可能含有不同的安全风险，需要确保每个源头的安全性并整合统一的安全策略。 6. **数据流处理**：数据流的动态性质要求动态的安全防护，如流分析和实时威胁检测。 7. **跨云迁移**：高频率的数据迁移可能导致数据暴露，需要有效的安全迁移策略和加密技术。 8. **隐私保护**：在大数据分析中保护个人隐私是一项重大挑战，需要确保匿名化和去标识化技术的有效性，同时遵守数据保护法规。 9. **访问控制和权限管理**：随着数据访问的广泛性，精确的访问控制和权限管理变得至关重要，以防止未经授权的访问和滥用。 10. **安全监控与审计**：大数据环境下的安全事件检测和响应需要高级的监控能力，以及强大的日志管理和审计功能，以追踪异常行为和进行事后分析。这些挑战要求开发新型的安全技术和策略，如基于行为分析的安全系统、自适应安全架构和利用机器学习进行威胁预测。同时，需要强化法律法规，确保大数据的收集、存储和使用符合隐私和数据保护标准。通过综合应对这些挑战，可以构建更强大、更安全的大数据生态系统。

CLOUD SECURITY ALLIANCE Expanded Top Ten Big Data Security and Privacy Challenges, April 2013

Figure 2: Classification of the Top 10 Challenges

In order to secure the infrastructure of Big Data systems, the distributed computations and data stores must be

secured. To secure the data itself, information dissemination must be privacy-preserving, and sensitive data must

be protected through the use of cryptography and granular access control. Managing the enormous volume of

data necessitates scalable and distributed solutions for both securing data stores and enabling efficient audits and

data provenance. Finally, the streaming data emerging from diverse end-points must be checked for integrity and

can be used to perform real time analytics for security incidents to ensure the health of the infrastructure.

Solving security and privacy challenges typically requires addressing three distinct issues:

1. Modeling: formalizing a threat model that covers most of the cyber-attack or data-leakage scenarios

2. Analysis: finding tractable solutions based on the threat model

3. Implementation: implementing the solution in existing infrastructures

In this paper, we provide a brief description of each challenge, review usage of Big Data that may be vulnerable,

and summarize existing knowledge according to the modeling, analysis, and implementation for each challenge.

Infrastructure

security

Secure Computations

in Distributed

Programming

Frameworks

Security Best

Practices for Non-

Relational Data

Stores

Data Privacy

Privacy Preserving

Data Mining and

Analytics

Cryptographically

Enforced Data Centric

Security

Granular Access

Control

Data

Management

Secure Data Storage

and Transaction Logs

Granular Audits

Data Provenance

Integrity and

Reactive

Security

End-point validation

and filtering

Real time Security

Monitoring

CLOUD SECURITY ALLIANCE Expanded Top Ten Big Data Security and Privacy Challenges, April 2013

1.0 Secure Computations in Distributed

Programming Frameworks

Distributed programming frameworks utilize parallel computation and storage to process massive amounts of

data. For example, the MapReduce framework splits an input file into multiple chunks. In the first phase of

MapReduce, a Mapper for each chunk reads the data, performs some computation, and outputs a list of key/value

pairs. In the next phase, a Reducer combines the values belonging to each distinct key and outputs the result.

There are two major attack prevention measures: securing the mappers and securing the data in the presence of

an untrusted mapper.

1.1 Use Case

Untrusted mappers can be altered to snoop on requests, alter MapReduce scripts, or alter results. The most

difficult problem is to detect mappers returning incorrect results, which will, in turn, generate incorrect aggregate

outputs. With large data sets, it is nearly impossible to identify malicious mappers that may create significant

damage, especially for scientific and financial computations.

Retailer consumer data is often analyzed by marketing agencies for targeted advertising or customer-segmenting.

These tasks involve highly parallel computations over large data sets and are particularly suited for MapReduce

frameworks such as Hadoop. However, the data mappers may contain intentional or unintentional leakages. For

example, a mapper may emit a unique value by analyzing a private record, undermining users’ privacy.

1.2 Modeling

The threat model for mappers has three major scenarios:

1. Malfunctioning Compute Worker Nodes – Workers assigned to mappers in a distributed computation

could malfunction due to incorrect configuration or a faulty node. A malfunctioning Worker could return

incorrect output from the mapper, which may compromise the integrity of the aggregate result. Such a

Worker may also be modified to leak users’ confidential data or profile users’ behaviors or preferences

for privacy mining.

2. Infrastructure Attacks – Compromised Worker nodes may tap the communication among other Workers

and the Master with the objective of replay, Man-In-the-Middle, and DoS attacks to the MapReduce

computations.

3. Rogue Data Nodes – Rogue data nodes can be added to a cluster, and subsequently receive replicated

data or deliver altered MapReduce code. The ability to create snapshots of legitimate nodes and re-

introduce altered copies is a straightforward attack in cloud and virtual environments and is difficult to

detect.

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