In today’s world of (network-, host-, and application-level) infrastructure security, data security becomes more important when using cloud computing at all “levels”: infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and software-as-a-service (SaaS). This chapter describes several aspects of data security, including:
Processing of data, including multitenancy
The objective of this chapter is to help users evaluate their data security scenarios and make informed judgments regarding risk for their organizations. As with other aspects of cloud computing and security, not all of these data security facets are of equal importance in all topologies (e.g., the use of a public cloud versus a private cloud, or non-sensitive data versus sensitive data).
With regard to data-in-transit, the primary risk is in not using a vetted encryption algorithm. Although this is obvious to information security professionals, it is not common for others to understand this requirement when using a public cloud, regardless of whether it is IaaS, PaaS, or SaaS. It is also important to ensure that a protocol provides confidentiality as well as integrity (e.g., FTP over SSL [FTPS], Hypertext Transfer Protocol Secure [HTTPS], and Secure Copy Program [SCP])—particularly if the protocol is used for transferring data across the Internet. Merely encrypting data and using a non-secured protocol (e.g., “vanilla” or “straight” FTP or HTTP) can provide confidentiality, but does not ensure the integrity of the data (e.g., with the use of symmetric streaming ciphers).
Although using encryption to protect data-at-rest might seem obvious, the reality is not that simple. If you are using an IaaS cloud service (public or private) for simple storage (e.g., Amazon’s Simple Storage Service or S3), encrypting data-at-rest is possible—and is strongly suggested. However, encrypting data-at-rest that a PaaS or SaaS cloud-based application is using (e.g., Google Apps, Salesforce.com) as a compensating control is not always feasible. Data-at-rest used by a cloud-based application is generally not encrypted, because encryption would prevent indexing or searching of that data.
Generally speaking, with data-at-rest, the economics of cloud computing are such that PaaS-based applications and SaaS use a multitenancy architecture. In other words, data, when processed by a cloud-based application or stored for use by a cloud-based application, is commingled with other users’ data (i.e., it is typically stored in a massive data store, such as Google’s BigTable). Although applications are often designed with features such as data tagging (see SaaS Application Security for further information) to prevent unauthorized access to commingled data, unauthorized access is still possible through some exploit of an application vulnerability (e.g., Google’s unauthorized data sharing between users of Documents and Spreadsheets in March 2009). Although some cloud providers have their applications reviewed by third parties or verified with third-party application security tools, data is not on a platform dedicated solely to one organization.
Although an organization’s data-in-transit might be encrypted during transfer to and from a cloud provider, and its data-at-rest might be encrypted if using simple storage (i.e., if it is not associated with a specification application), an organization’s data is definitely not encrypted if it is processed in the cloud (public or private). For any application to process data, that data must be unencrypted. Until June 2009, there was no known method for fully processing encrypted data. Therefore, unless the data is in the cloud for only simple storage, the data will be unencrypted during at least part of its life cycle in the cloud—processing at a minimum.
In June 2009, IBM announced that one of its researchers, working with a graduate student from Stanford University, had developed a fully homomorphic encryption scheme which allows data to be processed without being decrypted. This is a huge advance in cryptography, and it will have a significant positive impact on cloud computing as soon as it moves into deployment. Earlier work on fully homomorphic encryption (e.g., 2-DNF) was also conducted at Stanford University, but IBM’s announcement bettered even that promising work. Although the homomorphic scheme has broken the theoretical barrier to fully homomorphic encryption, it required immense computational effort. According to Ronald Rivest (MIT professor and coinventor of the famous RSA encryption scheme), the steps to make it practical won't be far behind. Other cryptographic research efforts are underway to limit the amount of data that would need to be decrypted for processing in the cloud, such as predicate encryption.
Whether the data an organization has put into the cloud is encrypted or not, it is useful and might be required (for audit or compliance purposes) to know exactly where and when the data was specifically located within the cloud. For example, the data might have been transferred to a cloud provider, such as Amazon Web Services (AWS), on date x1 at time y1 and stored in a bucket on Amazon’s S3 in example1.s3.amazonaws.com, then processed on date x2 at time y2 on an instance being used by an organization on Amazon’s Elastic Compute Cloud (EC2) in ec2-67-202-51-223.compute-1.amazonaws.com, then restored in another bucket, example2.s3.amazonaws.com, before being brought back into the organization for storage in an internal data warehouse belonging to the marketing operations group on date x3 at time y3. Following the path of data (mapping application data flows or data path visualization) is known as data lineage, and it is important for an auditor’s assurance (internal, external, and regulatory). However, providing data lineage to auditors or management is time-consuming, even when the environment is completely under an organization’s control. Trying to provide accurate reporting on data lineage for a public cloud service is really not possible. In the preceding example, on what physical system is that bucket on example1.s3.amazonaws.com, and specifically where is (or was) that system located? What was the state of that physical system then, and how would a customer or auditor verify that information?
Even if data lineage can be established in a public cloud, for some customers there is an even more challenging requirement and problem: proving data provenance—not just proving the integrity of the data, but the more specific provenance of the data. There is an important difference between the two terms. Integrity of data refers to data that has not been changed in an unauthorized manner or by an unauthorized person. Provenance means not only that the data has integrity, but also that it is computationally accurate; that is, the data was accurately calculated. For example, consider the following financial equation:
|SUM((((2*3)*4)/6)−2) = $2.00|
With that equation, the expected answer is $2.00. If the answer were different, there would be an integrity problem. Of course, the assumption is that the $2.00 is in U.S. dollars, but the assumption could be incorrect if a different dollar is used with the following associated assumptions:
The equation is specific to the Australian, Bahamian, Barbadian, Belize, Bermudian, Brunei, Canadian, Cayman Islands, Cook Islands, East Caribbean, Fijian, Guyanese, Hong Kong, Jamaican, Kiribati, Liberian, Namibian, New Zealand, Samoan, Singapore, Solomon Islands, Surinamese, New Taiwan, Trinidad and Tobago, Tuvaluan, or Zimbabwean dollar.
The dollar is meant to be converted from another country’s dollars into U.S. dollars.
The correct exchange rate is used and the conversion is calculated correctly and can be proven.
In this example, if the equation satisfies those assumptions, the equation has integrity but not provenance. There are many real-world examples in which data integrity is insufficient and data provenance is also required. Financial and scientific calculations are two obvious examples. How do you prove data provenance in a cloud computing scenario when you are using shared resources? Those resources are not under your physical or even logical control, and you probably have no ability to track the systems used or their state at the times you used them—even if you know some identifying information about the systems (e.g., their IP addresses) and the “general” location (e.g., a country, and not even a specific data center).
A final aspect of data security is data remanence. “Data remanence is the residual representation of data that has been in some way nominally erased or removed. This residue may be due to data being left intact by a nominal delete operation, or through physical properties of the storage medium. Data remanence may make inadvertent disclosure of sensitive information possible, should the storage media be released into an uncontrolled environment (e.g., thrown in the trash, or given to a third party).”
The risk posed by data remanence in cloud services is that an organization’s data can be inadvertently exposed to an unauthorized party—regardless of which cloud service you are using (SaaS, PaaS, or IaaS). When using SaaS or PaaS, the risk is almost certainly unintentional or inadvertent exposure. However, that is not reassuring after an unauthorized disclosure, and potential customers should question what third-party tools or reviews are used to help validate the security of the provider’s applications or platform.
In spite of the increased importance of data security, the attention that cloud service providers (CSPs) pay to data remanence is strikingly low. Many do not even mention data remanence in their services. And if the subject of data security is broached, many CSPs rather glibly refer to compliance with U.S. Department of Defense (DoD) 5220.22-M (the National Industrial Security Program Operating Manual). We say “glibly” because it appears that providers (and other information technology vendors) have not actually read this manual. DoD 5220.22-M states the two approved methods of data (destruction) security, but does not provide any specific requirements for how these two methods are to be achieved, nor does it provide any standards for how these methods are to be accomplished. Relevant information in DoD 5220.22-M regarding data remanence in this 141-page manual is limited to three paragraphs:
Clearing is the process of eradicating the data on media before reusing the media in an environment that provides an acceptable level of protection for the data that was on the media before clearing. All internal memory, buffer, or other reusable memory shall be cleared to effectively deny access to previously stored information.
Sanitization is the process of removing the data from media before reusing the media in an environment that does not provide an acceptable level of protection for the data that was on the media before sanitizing. IS resources shall be sanitized before they are released from classified information controls or released for use at a lower classification level.
For specific information about how data security should be achieved, providers should refer to the National Institute of Standards and Technology (NIST) Special Publication, 800-88, “Guidelines for Media Sanitization.” Although this NIST publication provides guidelines only, and is officially meant for federal civilian departments and agencies only, many companies, especially those in regulated industries, voluntarily adhere to NIST guidelines and standards. In the absence of any other industry standard for data remanence, adherence to these NIST guidelines is important.
If prospective customers of cloud computing services expect that data security will serve as compensating controls for possibly weakened infrastructure security, since part of a customer’s infrastructure security moves beyond its control and a provider’s infrastructure security may (for many enterprises) or may not (for small to medium-size businesses, or SMBs) be less robust than expectations, you will be disappointed. Although data-in-transit can and should be encrypted, any use of that data in the cloud, beyond simple storage, requires that it be decrypted. Therefore, it is almost certain that in the cloud, data will be unencrypted. And if you are using a PaaS-based application or SaaS, customer-unencrypted data will also almost certainly be hosted in a multitenancy environment (in public clouds). Add to that exposure the difficulties in determining the data’s lineage, data provenance—where necessary—and even many providers’ failure to adequately address such a basic security concern as data remanence, and the risks of data security for customers are significantly increased.
So, what should you do to mitigate these risks to data security? The only viable option for mitigation is to ensure that any sensitive or regulated data is not placed into a public cloud (or that you encrypt data placed into the cloud for simple storage only). Given the economic considerations of cloud computing today, as well as the present limits of cryptography, CSPs are not offering robust enough controls around data security. It may be that those economics change and that providers offer their current services, as well as a “regulatory cloud environment” (i.e., an environment where customers are willing to pay more for enhanced security controls to properly handle sensitive and regulated data). Currently, the only viable option for mitigation is to ensure that any sensitive or regulated data is not put into a public cloud.
In addition to the security of your own customer data, customers should also be concerned about what data the provider collects and how the CSP protects that data. Specifically with regard to your customer data, what metadata does the provider have about your data, how is it secured, and what access do you, the customer, have to that metadata? As your volume of data with a particular provider increases, so does the value of that metadata.
Additionally, your provider collects and must protect a huge amount of security-related data. For example, at the network level, your provider should be collecting, monitoring, and protecting firewall, intrusion prevention system (IPS), security incident and event management (SIEM), and router flow data. At the host level your provider should be collecting system logfiles, and at the application level SaaS providers should be collecting application log data, including authentication and authorization information.
What data your CSP collects and how it monitors and protects that data is important to the provider for its own audit purposes (e.g., SAS 70, as discussed in Chapter 8). Additionally, this information is important to both providers and customers in case it is needed for incident response and any digital forensics required for incident analysis.
For data stored in the cloud (i.e., storage-as-a-service), we are referring to IaaS and not data associated with an application running in the cloud on PaaS or SaaS. The same three information security concerns are associated with this data stored in the cloud (e.g., Amazon’s S3) as with data stored elsewhere: confidentiality, integrity, and availability.
When it comes to the confidentiality of data stored in a public cloud, you have two potential concerns. First, what access control exists to protect the data? Access control consists of both authentication and authorization. As we will discuss further in Chapter 5, CSPs generally use weak authentication mechanisms (e.g., username + password), and the authorization (“access”) controls available to users tend to be quite coarse and not very granular. For large organizations, this coarse authorization presents significant security concerns unto itself. Often, the only authorization levels cloud vendors provide are administrator authorization (i.e., the owner of the account itself) and user authorization (i.e., all other authorized users)—with no levels in between (e.g., business unit administrators, who are authorized to approve access for their own business unit personnel). Again, these access control issues are not unique to CSPs, and we discuss them in much greater detail in the following chapter.
What is definitely relevant to this section, however, is the second potential concern: how is the data that is stored in the cloud actually protected? For all practical purposes, protection of data stored in the cloud involves the use of encryption.
There has been some discussion in recent years about alternative data protection techniques; for example, in connection with the Data Accountability and Trust Act, reported in May 2006. These alternative techniques included indexing, masking, redaction, and truncation. However, there are no accepted standards for indexing, masking, redaction, or truncation—or any other data protection technique. The only data protection technique for which there are recognized standards is encryption, such as the NIST Federal Information Processing Standards (FIPS); see http://www.itl.nist.gov/fipspubs/.
So, is a customer’s data actually encrypted when it is stored in the cloud? And if so, with what encryption algorithm, and with what key strength? It depends, and specifically, it depends on which CSP you are using. For example, EMC’s MozyEnterprise does encrypt a customer’s data. However, AWS S3 does not encrypt a customer’s data. Customers are able to encrypt their own data themselves prior to uploading, but S3 does not provide encryption.
If a CSP does encrypt a customer’s data, the next consideration concerns what encryption algorithm it uses. Not all encryption algorithms are created equal. Cryptographically, many algorithms provide insufficient security. Only algorithms that have been publicly vetted by a formal standards body (e.g., NIST) or at least informally by the cryptographic community should be used. Any algorithm that is proprietary should absolutely be avoided. Note that we are talking about symmetric encryption algorithms here. Symmetric encryption (see Figure 4-1) involves the use of a single secret key for both the encryption and decryption of data. Only symmetric encryption has the speed and computational efficiency to handle encryption of large volumes of data. It would be highly unusual to use an asymmetric algorithm for this encryption use case. (See Figure 4-2.)
Although the example in Figure 4-1 is related to email, the same concept (i.e., a single shared, secret key) is used in data storage encryption.
Although the example in Figure 4-2 is related to email, the same concept (i.e., a public key and a private key) is not used in data storage encryption.
The next consideration for you is what key length is used. With symmetric encryption, the longer the key length (i.e., the greater number of bits in the key), the stronger the encryption. Although long key lengths provide more protection, they are also more computationally intensive, and may strain the capabilities of computer processors. What can be said is that key lengths should be a minimum of 112 bits for Triple DES (Data Encryption Standard) and 128-bits for AES (Advanced Encryption Standard)—both NIST-approved algorithms. For further information on key lengths, see NIST’s “Special Publication 800-57, Recommendation for Key Management—Part 1: General (Revised),” dated March 2007, at http://csrc.nist.gov/publications/nistpubs/800-57/sp800-57-Part1-revised2_Mar08-2007.pdf.
Another confidentiality consideration for encryption is key management. How are the encryption keys that are used going to be managed—and by whom? Are you going to manage your own keys? Hopefully, the answer is yes, and hopefully you have the expertise to manage your own keys. It is not recommended that you entrust a cloud provider to manage your keys—at least not the same provider that is handling your data. This means additional resources and capabilities are necessary. That being said, proper key management is a complex and difficult task. At a minimum, a customer should consult all three parts of NIST’s 800-57, “Recommendation for Key Management”:
“Part 1: General”
“Part 2: Best Practices for Key Management Organization”
“Part 3: Application-Specific Key Management Guidance (Draft)”
Because key management is complex and difficult for a single customer, it is even more complex and difficult for CSPs to try to properly manage multiple customers’ keys. For that reason, several CSPs do not do a good job of managing customers’ keys. For example, it is common for a provider to encrypt all of a customer’s data with a single key. Even worse, we are aware of one cloud storage provider that uses a single encryption key for all of its customers! The Organization for the Advancement of Structured Information Standards (OASIS) Key Management Interoperability Protocol (KMIP) is trying to address such issues; see http://www.oasis-open.org/committees/tc_home.php?wg_abbrev=kmip.
In addition to the confidentiality of your data, you also need to worry about the integrity of your data. Confidentiality does not imply integrity; data can be encrypted for confidentiality purposes, and yet you might not have a way to verify the integrity of that data. Encryption alone is sufficient for confidentiality, but integrity also requires the use of message authentication codes (MACs). The simplest way to use MACs on encrypted data is to use a block symmetric algorithm (as opposed to a streaming symmetric algorithm) in cipher block chaining (CBC) mode, and to include a one-way hash function. This is not for the cryptographically uninitiated—and it is one reason why effective key management is difficult. At the very least, cloud customers should be asking providers about these matters. Not only is this important for the integrity of a customer’s data, but it will also serve to provide insight on how sophisticated a provider’s security program is—or is not. Remember, however, that not all providers encrypt customer data, especially for PaaS and SaaS services.
Another aspect of data integrity is important, especially with bulk storage using IaaS. Once a customer has several gigabytes (or more) of its data up in the cloud for storage, how does the customer check on the integrity of the data stored there? There are IaaS transfer costs associated with moving data into and back down from the cloud, as well as network utilization (bandwidth) considerations for the customer’s own network. What a customer really wants to do is to validate the integrity of its data while that data remains in the cloud—without having to download and reupload that data.
This task is even more difficult because it must be done in the cloud without explicit knowledge of the whole data set. Customers generally do not know on which physical machines their data is stored, or where those systems are located. Additionally, that data set is probably dynamic and changing frequently. Those frequent changes obviate the effectiveness of traditional integrity insurance techniques.
What is needed instead is a proof of retrievability—that is, a mathematical way to verify the integrity of the data as it is dynamically stored in the cloud.
Assuming that a customer’s data has maintained its confidentiality and integrity, you must also be concerned about the availability of your data. There are currently three major threats in this regard—none of which are new to computing, but all of which take on increased importance in cloud computing because of increased risk.
The second threat to availability is the CSP’s own availability. No CSPs offer the sought-after “five 9s” (i.e., 99.999%) of uptime. A customer would be lucky to get “three 9s” of uptime. As Table 4-1 shows, there is a considerable difference between five 9s and three 9s.
Table 4-1. Percentage of uptime
Total downtime (HH:MM:SS)
A number of high-profile cloud provider outages have occurred. For example, Amazon’s S3 suffered a 2.5-hour outage in February 2008 and an eight-hour outage in July 2008. AWS is one of the more mature cloud providers, so imagine the difficulties that other, smaller or less mature cloud providers are having. These Amazon outages were all the more apparent because of the relatively large number of customers that the S3 service supports—and whom are highly (if not totally) reliant on S3’s availability for their own operations.
In addition to service outages, in some cases data stored in the cloud has actually been lost. For example, in March 2009, “cloud-based storage service provider Carbonite Inc. filed a lawsuit charging that faulty equipment from two hardware providers caused backup failures that resulted in the company losing data for 7,500 customers two years ago.”
A larger question for cloud customers to consider is whether cloud storage providers will even be in business in the future. In February 2009, cloud provider Coghead suddenly shut down, giving its customers fewer than 90 days (nine weeks) to get their data off its servers—or lose it altogether.
Finally, prospective cloud storage customers must be certain to ascertain just what services their provider is actually offering. Cloud storage does not mean the stored data is actually backed up. Some cloud storage providers do back up customer data, in addition to providing storage. However, many cloud storage providers do not back up customer data, or do so only as an additional service for an additional cost. For example, “data stored in Amazon S3, Amazon SimpleDB, or Amazon Elastic Block Store is redundantly stored in multiple physical locations as a normal part of those services and at no additional charge.” However, “data that is maintained within running instances on Amazon EC2, or within Amazon S3 and Amazon SimpleDB, is all customer data and therefore AWS does not perform backups.” For availability, this is a seemingly simple yet critical question that customers should be asking of cloud storage providers.
All three of these considerations (confidentiality, integrity, and availability) should be encapsulated in a CSP’s service-level agreement (SLA) to its customers. However, at this time, CSP SLAs are extremely weak—in fact, for all practical purposes, they are essentially worthless. Even where a CSP appears to have at least a partially sufficient SLA, how that SLA actually gets measured is problematic. For all of these reasons, data security considerations and how data is actually stored in the cloud should merit considerable attention by customers.
In this chapter we looked at aspects of customer data security, including the security of the customer data itself, as well as metadata about that data. As noted, in addition to being concerned about your own customer data, customers also need to take interest in providers’ data collection efforts, the monitoring of that data, and its security. Much provider data would be necessary for incident response and digital forensic analysis in the event of an incident (e.g., a possible compromise) involving a customer’s own data.
The primary means of data security mitigation at this time is encryption—when it is used. Until the June 2009 announcement of a fully homomorphic encryption scheme, it was necessary to decrypt data for processing (except for relatively simple operations, such as supporting addition operation and one multiplication operation). With fully homomorphic encryption (it may take a few years to make this practical for commercial use), decrypting data for processing no longer is an issue unto itself, but another related concern still persists: key management.
As we discussed, key management is a significant problem today for enterprises, and even more of a problem for CSPs. Scalability is an issue, as well as the complexity of managing a huge number of keys for a large number of customers. Of course, some CSPs will take a far simpler approach to key management—and one that potentially puts your data at greater risk. Remember, you could end up effectively destroying your own data if you have a key management failure (e.g., you lose your keys).
Talk of alternative methods of data protection, such as redaction, truncations, obfuscation, and others, should be viewed with great concern. Not only are there no accepted standards for these alternative methods, but also there are no programs to validate the implementations of whatever could possibly be developed.
Do these concerns about data security negate the value of storage-as-a-service in the cloud? No, but they do mean that customers need to pay close attention to the security of their data. Is that data encrypted? If so, by whom? And who is responsible for key management, and how will that be accomplished specifically?
Given the large number of issues concerning data security, customers concerned about the security afforded by infrastructure security and who are counting on data security to provide compensating controls will almost certainly be disappointed. Data security is a significant task, with a lot of complexity, and it is just as important for customers to evaluate this thoroughly as the more traditional aspects of infrastructure security. It’s your data and you should make significant efforts to protect it, as well as ensuring that your provider is protecting your data as well as its own data.
 For example, see “IBM Discovers Encryption Scheme That Could Improve Cloud Security, Spam Filtering,” at http://www.eweek.com/c/a/Security/IBM-Uncovers-Encryption-Scheme-That-Could-Improve-Cloud-Security-Spam-Filtering-135413/.
 2-DNF (disjunctive normal form) is an example of homomorphic encryption that enables “computing with encrypted data.” See “Evaluating 2-DNF Formulas on Ciphertexts” by Dan Boneh, Eu-Jin Goh, and Kobbi Nissim, at http://crypto.stanford.edu/~dabo/papers/2dnf.pdf.
 Predicate encryption is a form of asymmetric encryption whereby different individuals (or groups) can selectively decrypt encrypted data instead of decrypting all of it. See “Predicate Encryption Supporting Disjunctions, Polynomial Equations, and Inner Products” by Jonathan Katz, Amit Sahai, and Brent Waters, at http://eprint.iacr.org/2007/404.pdf.
 DoD 5220.22-M, National Industrial Security Program Operating Manual, dated February 28, 2006.
 Published in September 2006; see http://csrc.nist.gov/publications/nistpubs/800-88/NISTSP800-88_rev1.pdf.
 For example, as of April 2009, AWS S3 charges $0.100 per gigabyte for all data transferred in, and $0.170 per gigabyte (for the first 10 TB) per month for all data transferred out.
 For more information on proofs of retrievability, see the academic paper “Ensuring Data Storage Security in Cloud Computing” by Cong Wang, Qian Wang, Kui Ren, and Wenjing Lou, published in 2009.
 See “Latest cloud storage hiccups prompt data security questions,” ComputerWorld, March 27, 2009.
 “Amazon Web Services: Overview of Security Processes,” September 2008, page 3.